# import all necessary libraries
import copy
import os
from PIL import Image
from IPython.display import Image
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from sklearn import metrics
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC
from torch import nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.tensorboard import SummaryWriter


# Display all columns
pd.options.display.max_columns = None
pd.options.display.max_rows = None

# mount drive
# from google.colab import drive
# drive.mount('/content/drive')

# data_raw = pd.read_json(r'/content/drive/MyDrive/HochschuleKiel/DeepLearning/Portfolio Exam/data/data_filtered.json', orient='records')

data_raw = pd.read_json(r'../data/data_filtered.json', orient='records')

display(data_raw.head(3))

print(f'Shape of the data: {data_raw.shape}')
display(data_raw.info())
display(data_raw.describe(include='object'))
display(data_raw.describe(include='number'))

Shape of the data: (3656, 71)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3656 entries, 0 to 3655
Data columns (total 71 columns):
 #   Column                     Non-Null Count  Dtype 
---  ------                     --------------  ----- 
 0   age                        3656 non-null   int64 
 1   subject                    3656 non-null   object
 2   sex                        3656 non-null   object
 3   created                    3656 non-null   object
 4   cycle                      3656 non-null   object
 5   Left Ankle Angles_X        3656 non-null   object
 6   Left Ankle Angles_Y        3656 non-null   object
 7   Left Ankle Angles_Z        3656 non-null   object
 8   Left CenterOfMass_corr_X   3656 non-null   object
 9   Left CenterOfMass_corr_Y   3656 non-null   object
 10  Left CenterOfMass_corr_Z   3656 non-null   object
 11  Left Elbow Angles_X        3656 non-null   object
 12  Left Elbow Angles_Y        3656 non-null   object
 13  Left Elbow Angles_Z        3656 non-null   object
 14  Left Foot Pitch Angles_X   3656 non-null   object
 15  Left Foot Pitch Angles_Y   3656 non-null   object
 16  Left Foot Pitch Angles_Z   3656 non-null   object
 17  Left Foot Progression_X    3656 non-null   object
 18  Left Foot Progression_Y    3656 non-null   object
 19  Left Foot Progression_Z    3656 non-null   object
 20  Left Hip Angles_X          3656 non-null   object
 21  Left Hip Angles_Y          3656 non-null   object
 22  Left Hip Angles_Z          3656 non-null   object
 23  Left Knee Angles_X         3656 non-null   object
 24  Left Knee Angles_Y         3656 non-null   object
 25  Left Knee Angles_Z         3656 non-null   object
 26  Left Pelvic Angles_X       3656 non-null   object
 27  Left Pelvic Angles_Y       3656 non-null   object
 28  Left Pelvic Angles_Z       3656 non-null   object
 29  Left Shoulder Angles_X     3656 non-null   object
 30  Left Shoulder Angles_Y     3656 non-null   object
 31  Left Shoulder Angles_Z     3656 non-null   object
 32  Left Thorax Angles_X       3656 non-null   object
 33  Left Thorax Angles_Y       3656 non-null   object
 34  Left Thorax Angles_Z       3656 non-null   object
 35  Left Thorax_Lab Angles_X   3656 non-null   object
 36  Left Thorax_Lab Angles_Y   3656 non-null   object
 37  Left Thorax_Lab Angles_Z   3656 non-null   object
 38  Right Ankle Angles_X       3656 non-null   object
 39  Right Ankle Angles_Y       3656 non-null   object
 40  Right Ankle Angles_Z       3656 non-null   object
 41  Right CenterOfMass_corr_X  3656 non-null   object
 42  Right CenterOfMass_corr_Y  3656 non-null   object
 43  Right CenterOfMass_corr_Z  3656 non-null   object
 44  Right Elbow Angles_X       3656 non-null   object
 45  Right Elbow Angles_Y       3656 non-null   object
 46  Right Elbow Angles_Z       3656 non-null   object
 47  Right Foot Pitch Angles_X  3656 non-null   object
 48  Right Foot Pitch Angles_Y  3656 non-null   object
 49  Right Foot Pitch Angles_Z  3656 non-null   object
 50  Right Foot Progression_X   3656 non-null   object
 51  Right Foot Progression_Y   3656 non-null   object
 52  Right Foot Progression_Z   3656 non-null   object
 53  Right Hip Angles_X         3656 non-null   object
 54  Right Hip Angles_Y         3656 non-null   object
 55  Right Hip Angles_Z         3656 non-null   object
 56  Right Knee Angles_X        3656 non-null   object
 57  Right Knee Angles_Y        3656 non-null   object
 58  Right Knee Angles_Z        3656 non-null   object
 59  Right Pelvic Angles_X      3656 non-null   object
 60  Right Pelvic Angles_Y      3656 non-null   object
 61  Right Pelvic Angles_Z      3656 non-null   object
 62  Right Shoulder Angles_X    3656 non-null   object
 63  Right Shoulder Angles_Y    3656 non-null   object
 64  Right Shoulder Angles_Z    3656 non-null   object
 65  Right Thorax Angles_X      3656 non-null   object
 66  Right Thorax Angles_Y      3656 non-null   object
 67  Right Thorax Angles_Z      3656 non-null   object
 68  Right Thorax_Lab Angles_X  3656 non-null   object
 69  Right Thorax_Lab Angles_Y  3656 non-null   object
 70  Right Thorax_Lab Angles_Z  3656 non-null   object
dtypes: int64(1), object(70)
memory usage: 2.0+ MB

None

display(data_raw.head(2))

data_raw['session_name'] = data_raw['subject'] + '_' + data_raw['created']

data_raw.drop(['subject', 'created'], axis=1, inplace=True)

cols = data_raw.columns.tolist()
col_to_check = cols[4:] # check all columns with the angles
# Überprüfen, ob alle Listen die gleiche Länge haben
lengths = data_raw[col_to_check].map(len)
print(lengths.value_counts())

# Konvertieren aller Listen in numpy-Arrays
data_raw[col_to_check] = data_raw[col_to_check].map(lambda x: np.array(x) if isinstance(x, list) else x)

# Überprüfen, ob es NaN-Werte in den Listen gibt
nan_counts = data_raw[col_to_check].map(lambda x: np.isnan(x).any() if isinstance(x, np.ndarray) else False)
print(nan_counts.value_counts())

Left Ankle Angles_Y  Left Ankle Angles_Z  Left CenterOfMass_corr_X  Left CenterOfMass_corr_Y  Left CenterOfMass_corr_Z  Left Elbow Angles_X  Left Elbow Angles_Y  Left Elbow Angles_Z  Left Foot Pitch Angles_X  Left Foot Pitch Angles_Y  Left Foot Pitch Angles_Z  Left Foot Progression_X  Left Foot Progression_Y  Left Foot Progression_Z  Left Hip Angles_X  Left Hip Angles_Y  Left Hip Angles_Z  Left Knee Angles_X  Left Knee Angles_Y  Left Knee Angles_Z  Left Pelvic Angles_X  Left Pelvic Angles_Y  Left Pelvic Angles_Z  Left Shoulder Angles_X  Left Shoulder Angles_Y  Left Shoulder Angles_Z  Left Thorax Angles_X  Left Thorax Angles_Y  Left Thorax Angles_Z  Left Thorax_Lab Angles_X  Left Thorax_Lab Angles_Y  Left Thorax_Lab Angles_Z  Right Ankle Angles_X  Right Ankle Angles_Y  Right Ankle Angles_Z  Right CenterOfMass_corr_X  Right CenterOfMass_corr_Y  Right CenterOfMass_corr_Z  Right Elbow Angles_X  Right Elbow Angles_Y  Right Elbow Angles_Z  Right Foot Pitch Angles_X  Right Foot Pitch Angles_Y  Right Foot Pitch Angles_Z  Right Foot Progression_X  Right Foot Progression_Y  Right Foot Progression_Z  Right Hip Angles_X  Right Hip Angles_Y  Right Hip Angles_Z  Right Knee Angles_X  Right Knee Angles_Y  Right Knee Angles_Z  Right Pelvic Angles_X  Right Pelvic Angles_Y  Right Pelvic Angles_Z  Right Shoulder Angles_X  Right Shoulder Angles_Y  Right Shoulder Angles_Z  Right Thorax Angles_X  Right Thorax Angles_Y  Right Thorax Angles_Z  Right Thorax_Lab Angles_X  Right Thorax_Lab Angles_Y  Right Thorax_Lab Angles_Z  session_name
101                  101                  101                       101                       101                       101                  101                  101                  101                       101                       101                       101                      101                      101                      101                101                101                101                 101                 101                 101                   101                   101                   101                     101                     101                     101                   101                   101                   101                       101                       101                       101                   101                   101                   101                        101                        101                        101                   101                   101                   101                        101                        101                        101                       101                       101                       101                 101                 101                 101                  101                  101                  101                    101                    101                    101                      101                      101                      101                    101                    101                    101                        101                        101                        27              3656
Name: count, dtype: int64
Left Ankle Angles_Y  Left Ankle Angles_Z  Left CenterOfMass_corr_X  Left CenterOfMass_corr_Y  Left CenterOfMass_corr_Z  Left Elbow Angles_X  Left Elbow Angles_Y  Left Elbow Angles_Z  Left Foot Pitch Angles_X  Left Foot Pitch Angles_Y  Left Foot Pitch Angles_Z  Left Foot Progression_X  Left Foot Progression_Y  Left Foot Progression_Z  Left Hip Angles_X  Left Hip Angles_Y  Left Hip Angles_Z  Left Knee Angles_X  Left Knee Angles_Y  Left Knee Angles_Z  Left Pelvic Angles_X  Left Pelvic Angles_Y  Left Pelvic Angles_Z  Left Shoulder Angles_X  Left Shoulder Angles_Y  Left Shoulder Angles_Z  Left Thorax Angles_X  Left Thorax Angles_Y  Left Thorax Angles_Z  Left Thorax_Lab Angles_X  Left Thorax_Lab Angles_Y  Left Thorax_Lab Angles_Z  Right Ankle Angles_X  Right Ankle Angles_Y  Right Ankle Angles_Z  Right CenterOfMass_corr_X  Right CenterOfMass_corr_Y  Right CenterOfMass_corr_Z  Right Elbow Angles_X  Right Elbow Angles_Y  Right Elbow Angles_Z  Right Foot Pitch Angles_X  Right Foot Pitch Angles_Y  Right Foot Pitch Angles_Z  Right Foot Progression_X  Right Foot Progression_Y  Right Foot Progression_Z  Right Hip Angles_X  Right Hip Angles_Y  Right Hip Angles_Z  Right Knee Angles_X  Right Knee Angles_Y  Right Knee Angles_Z  Right Pelvic Angles_X  Right Pelvic Angles_Y  Right Pelvic Angles_Z  Right Shoulder Angles_X  Right Shoulder Angles_Y  Right Shoulder Angles_Z  Right Thorax Angles_X  Right Thorax Angles_Y  Right Thorax Angles_Z  Right Thorax_Lab Angles_X  Right Thorax_Lab Angles_Y  Right Thorax_Lab Angles_Z  session_name
False                False                False                     False                     False                     False                False                False                False                     False                     False                     False                    False                    False                    False              False              False              False               False               False               False                 False                 False                 False                   False                   False                   False                 False                 False                 False                     False                     False                     False                 False                 False                 False                      False                      False                      False                 False                 False                 False                      False                      False                      False                     False                     False                     False               False               False               False                False                False                False                  False                  False                  False                    False                    False                    False                  False                  False                  False                      False                      False                      False           3656
Name: count, dtype: int64

# bring the last column to the first position
cols = data_raw.columns.tolist()
cols = cols[-1:] + cols[:-1]
data_raw = data_raw[cols]
display(data_raw.head(3))

df_age = data_raw.groupby(by=['sex', 'session_name']).agg({'age': 'first'}).reset_index().sort_values(by=['sex', 'age'])
display(df_age.describe())

print(f'Shape before filter: {data_raw.shape}')
data_raw = data_raw.loc[data_raw['age'] >= 5]
print(f'Shape after filter: {data_raw.shape}')

Shape before filter: (3656, 70)
Shape after filter: (3471, 70)

display(data_raw.age.describe())
sns.violinplot(df_age, x='sex', y='age', hue='sex')
plt.title('Age Distribution for each Gender')
plt.show()

count    3471.000000
mean       57.238548
std        21.738467
min         6.000000
25%        39.000000
50%        63.000000
75%        74.000000
max        93.000000
Name: age, dtype: float64

df_plot = data_raw.groupby(by=['sex']).agg({'session_name': 'count'}).reset_index()

fig = plt.figure(figsize=(10, 5))

sns.barplot(df_plot, x='sex', y='session_name', hue='sex')

# set title
plt.title('Number of Gait Cycles for Females and Males')
plt.ylabel('Number of gait cycles')
plt.xlabel('Sex')
plt.show()

def get_mean_std(data: pd.DataFrame, joint_name: str, gender: str):
    """
    Calculates the mean and standard deviation of gait cycle values for a specified joint and gender.
    """
    matrix = []
    for row in data.loc[data['sex'] == gender][joint_name]:
        matrix.append(row)

    matrix = np.array(matrix)
    matrix.shape

    std = np.std(matrix, axis=0)
    mean = np.mean(matrix, axis=0)

    x = np.arange(0, len(mean))

    return x, mean, std

# Get all column names for the angles
colnames = data_raw.columns.tolist()[4:]

# groups of 3 columns for each joint (X, Y, Z)
joint_axes_groups = [
    colnames[i:i + 3] for i in range(0, len(colnames), 3)
]
n_rows = len(joint_axes_groups)  # number of joints
n_cols = 3  # X, Y, Z

fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 5 * n_rows))

# Plotting
for i, joint_axes in enumerate(joint_axes_groups):
    for j, col in enumerate(joint_axes):
        ax = axes[i, j]

        # Females
        color = 'mediumblue'
        x, mean, std = get_mean_std(data_raw, col, 'female')
        ax.plot(x, mean, color=color)
        ax.fill_between(x, mean - std, mean + std, alpha=0.3, color=color)

        # Males
        color = 'tomato'
        x, mean, std = get_mean_std(data_raw, col, 'male')
        ax.plot(x, mean, color=color)
        ax.fill_between(x, mean - std, mean + std, alpha=0.3, color=color)

        # Titel and labels
        ax.set_title(col)
        ax.set_xlabel('Gait Cycle')
        ax.set_ylabel('Angle')

plt.tight_layout()
plt.show()

def get_matrix(data: pd.DataFrame, joint_name: str):
    """
    Calculates the mean and standard deviation of gait cycle values for a specified joint and gender.
    """
    matrix = []
    for row in data[joint_name]:
        matrix.append(row)

    matrix = np.array(matrix)


    return matrix


# Get all column names for the angles
colnames = data_raw.columns.tolist()[4:]

arrays = []
for col in colnames:
    matrix = get_matrix(data_raw, col)
    arrays.append(matrix)

# Stack arrays along a new dimension, resulting in a 3D array
stacked = np.stack(arrays, axis=0)
print(f'Shape of stacked array: {stacked.shape}')
print(stacked.reshape(len(stacked), -1).shape)

# Calculate element-wise correlation across the arrays
corr = np.corrcoef(stacked.reshape(len(stacked), -1))

Shape of stacked array: (66, 3471, 101)
(66, 350571)

mask = np.triu(np.ones_like(corr, dtype=bool))  # Source: https://stackoverflow.com/questions/2318529/plotting-only-upper-lower-triangle-of-a-heatmap

# Plot heatmap
plt.figure(figsize=(30, 30))
sns.heatmap(corr, annot=True, cmap='coolwarm', linewidth=0.5, fmt='.2f', annot_kws={"size": 8},
            mask=mask, vmin=-1, vmax=1,
            xticklabels=colnames, yticklabels=colnames
            )

plt.title('Correlation Matrix of the Gait Cylces', fontsize=14, fontweight='bold')
plt.xticks(fontsize=12, rotation=90)
plt.yticks(fontsize=12, rotation=0)
plt.show()

target = data_raw['sex'] # define the target
target = target.map({'female': 1, 'male': 0}) # assign 1 for female and 1 for male
data = data_raw.drop(columns=['sex', 'session_name', 'cycle']) # drop the column sex, cylce and session_name from the data
data['age'] = data['age'].astype('int') # convert the age to integer

print('Data Head')
display(data.head(4))
print('Target Head')
display(target.head(4))
print(f'Shape of the data: {data.shape}')
print(f'Shape of the target: {target.shape}')

Data Head

Target Head

0    0
1    0
2    0
3    0
Name: sex, dtype: int64

Shape of the data: (3471, 67)
Shape of the target: (3471,)

gait_data = data.drop(columns=['age']) # drop the age column

age_data = data['age']

display(gait_data.head(2))

expanded_features = []

# Convert each column to a numpy array and append it to the list
for col in gait_data.columns:
    expanded_col = np.array(gait_data[col].to_list())
    expanded_features.append(expanded_col)

# Stack the arrays in the list to create a 3D numpy array
expanded_data = np.hstack(expanded_features)
gait_data = expanded_data.reshape(len(expanded_data), -1, len(gait_data.columns))


# sclicing: [gait cylces, 101 angles, features]
print(gait_data[:,:,:].shape)

(3471, 101, 66)

age_data = age_data.to_numpy().reshape(-1, 1)

# Reshape von age_data
age_data_expanded = np.repeat(age_data[:, np.newaxis, :], gait_data.shape[1], axis=1)  # Shape (3916, 101, 1)
print(f'Shape of the age data: {age_data_expanded.shape}')

# Concatenation von gait_data und age_data
combined_data = np.concatenate([gait_data, age_data_expanded], axis=-1)  # Shape (3916, 101, 67)
print(f'Shape of the combined data: {combined_data.shape}')

Shape of the age data: (3471, 101, 1)
Shape of the combined data: (3471, 101, 67)

# reshape the gait_data for train, test split
data = combined_data.reshape(len(gait_data), -1)
target = target.to_numpy().reshape(-1, 1)

print(f'Shape of the data: {data.shape}')
print(f'Shape of the target: {target.shape}')

# Split the data into train, test and validation set
data_train, data_temp, target_train, target_temp = train_test_split(data, target, test_size=0.4, random_state=1, stratify=target)
data_val, data_test, target_val, target_test = train_test_split(data_temp, target_temp, test_size=0.5, random_state=1, stratify=target_temp)

# scale the data
scaler = MinMaxScaler()
X_train = scaler.fit_transform(data_train)
X_val = scaler.transform(data_val)
X_test = scaler.transform(data_test)

print(f'Shape of the train data: {data_train.shape}')
print(f'Shape of the validation data: {data_val.shape}')
print(f'Shape of the test data: {data_test.shape}')
print(f'Shape of the train target: {target_train.shape}')
print(f'Shape of the validation target: {target_val.shape}')
print(f'Shape of the test target: {target_test.shape}')

Shape of the data: (3471, 6767)
Shape of the target: (3471, 1)
Shape of the train data: (2082, 6767)
Shape of the validation data: (694, 6767)
Shape of the test data: (695, 6767)
Shape of the train target: (2082, 1)
Shape of the validation target: (694, 1)
Shape of the test target: (695, 1)

# Count unique values and their frequencies
unique, counts = np.unique(target_train, return_counts=True)

max_count_index = np.argmax(counts)
most_frequent = unique[max_count_index]
most_frequent_count = counts[max_count_index]

print(f"Most frequent Class: {most_frequent}, Count: {most_frequent_count}")


# Simple Heuristic
most_frequent_clf = DummyClassifier(strategy='most_frequent')
most_frequent_clf.fit(data_train, target_train)

most_frequent_test_pred = most_frequent_clf.predict(data_test)
most_frequent_val_pred = most_frequent_clf.predict(data_val)
most_frequent_train_pred = most_frequent_clf.predict(data_train)


# This function is used to store the results of the experiments in a table
def add_results(results, algorithm,
                target_train, target_train_pred,
                target_val, target_val_pred,
                target_test, target_test_pred):
    '''
    Create a table with evaluation results
    of a regression experiment
    '''
    for dataset, actual, predicted in zip(
        ("train", 'val', "test"),
        (target_train, target_val, target_test),
        (target_train_pred, target_val_pred, target_test_pred)):

        results= pd.concat([results, pd.DataFrame([{
            "algorithm": algorithm,
            "dataset": dataset,
            "Accuracy": round(accuracy_score(actual, predicted)*100, 3),
            "AUC": round(roc_auc_score(actual, predicted)*100, 3),
        }])], ignore_index=True)
    return results

# Calculate accuracy
results =[]
results = add_results(pd.DataFrame(), "Baseline",
                      target_train=target_train,
                      target_train_pred=most_frequent_train_pred,
                      target_val=target_val,
                      target_val_pred=most_frequent_val_pred,
                      target_test=target_test,
                      target_test_pred=most_frequent_test_pred)


display(results)

Most frequent Class: 1, Count: 1077

dt_clf = SVC()
dt_clf.fit(data_train, target_train.reshape(-1))
dt_test_pred = dt_clf.predict(data_test)
dt_val_pred = dt_clf.predict(data_val)
dt_train_pred = dt_clf.predict(data_train)

results = add_results(results, "SVM",
                      target_train=target_train,
                      target_train_pred=dt_train_pred,
                      target_val=target_val,
                      target_val_pred=dt_val_pred,
                      target_test=target_test,
                      target_test_pred=dt_test_pred)
display(results)

# Create Tensors
data_train_tensor = torch.tensor(data_train, dtype=torch.float32)
data_test_tensor = torch.tensor(data_test, dtype=torch.float32)
data_val_tensor = torch.tensor(data_val, dtype=torch.float32)

target_train_tensor = torch.tensor(target_train, dtype=torch.float32)
target_test_tensor = torch.tensor(target_test, dtype=torch.float32)
target_val_tensor = torch.tensor(target_val, dtype=torch.float32)

# Visualize the shape
print(f'data_train_tensor shape: {data_train_tensor.shape}, type: {data_train_tensor.dtype}')
print(f'data_test_tensor shape: {data_test_tensor.shape}, type: {data_test_tensor.dtype}')
print(f'data_val_tensor shape: {data_val_tensor.shape}, type: {data_val_tensor.dtype}\n')

print(f'target_train_tensor shape: {target_train_tensor.shape}, type: {target_train_tensor.dtype}')
print(f'target_test_tensor shape: {target_test_tensor.shape}, type: {target_test_tensor.dtype}')
print(f'target_val_tensor shape: {target_val_tensor.shape}, type: {target_val_tensor.dtype}')

data_train_tensor shape: torch.Size([2082, 6767]), type: torch.float32
data_test_tensor shape: torch.Size([695, 6767]), type: torch.float32
data_val_tensor shape: torch.Size([694, 6767]), type: torch.float32

target_train_tensor shape: torch.Size([2082, 1]), type: torch.float32
target_test_tensor shape: torch.Size([695, 1]), type: torch.float32
target_val_tensor shape: torch.Size([694, 1]), type: torch.float32

def get_data(combined_data):
  """
  Helper function to get the data into the right shape.
  """

  combined_data = combined_data.reshape(len(combined_data),101, -1)

  # bring the data into shape (age_data, joint_data, axis_data, gait_cycle_data)
  original_gait_data = combined_data[:, :, :-1]  # Shape (3916, 101, 66)
  original_age_data = combined_data[:, 0, -1]    # Shape (3916,)

  # Reshape age_data to (batch_size, 1)
  original_age_data = original_age_data.unsqueeze(1)  # Add a dimension for features

  return original_age_data, original_gait_data

# randomstate for torch
torch.manual_seed(1)

<torch._C.Generator at 0x148a7188130>

class GaitGenderClassifier(nn.Module):
    def __init__(self, dropout_prob):
        super(GaitGenderClassifier, self).__init__()

        self.lstm = nn.LSTM(input_size=66, hidden_size=128, num_layers=2, batch_first=True)

        self.age_fc = nn.Sequential(
            nn.Linear(1, 16),
            nn.LeakyReLU(),
            nn.Dropout(p=dropout_prob)
            )

        self.fc1 = nn.Sequential(
            nn.Linear(128 + 16, 64),
            nn.LeakyReLU(),
            nn.Dropout(p=dropout_prob)
            )

        self.fc2 = nn.Sequential(
            nn.Linear(64, 32),
            nn.LeakyReLU(),
            nn.Dropout(p=dropout_prob)
            )

        self.fc3 = nn.Sequential(
            nn.Linear(32, 8),
            nn.LeakyReLU(),
            nn.Dropout(p=dropout_prob)
            )

        self.output = nn.Linear(8, 1)

    def forward(self, age, gait_cycle):
        batch_size = gait_cycle.shape[0]
        h_0 = torch.zeros(2, batch_size, 128).to(gait_cycle.device)
        c_0 = torch.zeros(2, batch_size, 128).to(gait_cycle.device)

        x, _ = self.lstm(gait_cycle, (h_0, c_0))
        x = x[:, -1, :]  # Take the output from the last time step

        age_out = self.age_fc(age)

        combined = torch.cat((x, age_out), dim=1)

        x = self.fc1(combined)
        x = self.fc2(x)
        x = self.fc3(x)

        output = self.output(x)
        return output

    def predict(self, age, gait_cycle, threshold=0.5):
        self.eval()  # Set model to evaluation mode
        with torch.no_grad():
            logits = self.forward(age, gait_cycle)  # Get raw logits
            probabilities = torch.sigmoid(logits)  # Apply sigmoid to get probabilities
            predictions = (probabilities >= threshold).float()  # Apply threshold for binary classification
            return predictions

if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')

X_train, y_train, X_val, y_val = X_train.to(device), y_train.to(device), X_val.to(device), y_val.to(device)
model.to(device)

dropout_results = [] # storing the results of the dropout experiment

def training(model, X_train, y_train, X_val, y_val, criterion, optimizer, epochs, writer, batch_size=64, patience=50, dropout_prob=0.5, log_tensorboard=True, log_dropout=True, verbose=True):

    # Set device to GPU if available
    if torch.cuda.is_available():
        device = torch.device('cuda')
    else:
        device = torch.device('cpu')

    # Move data and model to device
    X_train, y_train, X_val, y_val = X_train.to(device), y_train.to(device), X_val.to(device), y_val.to(device)
    model.to(device)

    # Initialize best metrics and model
    best_eval_metric_val = -float('inf') # Initialize the best_eval_metric
    best_val_loss = float('inf') # Initialize the best_val_loss
    best_model = copy.deepcopy(model.state_dict()) # Initialize the best_model
    patience_counter_loss = 0  # Early stopping counter
    patience_counter_metric = 0  # Early stopping counter

    # DataLoader for batching
    train_dataset = TensorDataset(X_train, y_train)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)


    # Scheduler for learning rate adjustment (if val_loss does not improve for 'patience' epochs)
    scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=3, factor=0.5)
    train_losses, val_losses = [], [] # to visualize the drop_out_prob
    train_accuracies, val_accuracies = [], []
    # Training Loop
    for epoch in range(epochs):
        model.train()
        epoch_loss = 0

        # Training loop
        for data, target in train_loader:
            data, target = data.to(device), target.to(device)

            optimizer.zero_grad()  # Clear gradients
            outputs = model(*get_data(data))  # Forward pass
            loss = criterion(outputs, target)  # Calculate loss
            loss.backward()  # Backpropagation
            optimizer.step()  # Update weights

            epoch_loss += loss.item()  # Accumulate batch loss

        # Validation
        model.eval()
        with torch.no_grad():
            val_outputs = model(*get_data(X_val))  # Forward pass

            loss_val = criterion(val_outputs, y_val)
            scheduler.step(loss_val)  # Adjust learning rate based on loss

            # calculate the training accuracy
            predictions_train = model.predict(*get_data(X_train))
            accuracy_train = accuracy_score(y_train.cpu(), predictions_train.cpu())

            # Calculate validation accuracy
            predictions_val = model.predict(*get_data(X_val))
            accuracy_val = accuracy_score(y_val.cpu(), predictions_val.cpu())

            # Log to dropout_results
            if log_dropout:
              train_losses.append(epoch_loss / len(train_loader))
              val_losses.append(loss_val.item())
              train_accuracies.append(accuracy_train)
              val_accuracies.append(accuracy_val)

            # Log to TensorBoard
            if log_tensorboard:
              writer.add_scalars("Loss", {"train": epoch_loss / len(train_loader), 'val': loss_val}, epoch)
              writer.add_scalars("Accuracy", {"train": accuracy_train, "val": accuracy_val}, epoch)
              #writer.add_scalar("Accuracy/val", accuracy_val, epoch)
              writer.flush()

            # Early stopping if loss_val is increasing
            if loss_val < best_val_loss:
                best_val_loss = loss_val  # Update best val_loss
                patience_counter_loss = 0  # Reset patience counter
            else:
                patience_counter_loss += 1  # Increment if no improvement

            # Early Stopping based on if val_acc is not increasing
            if accuracy_val > best_eval_metric_val:
                best_eval_metric_val = accuracy_val
                best_model = copy.deepcopy(model.state_dict()) # saves the best model where the accuracy_val is highest
                patience_counter_metric = 0  # Reset patience counter if improved
            else:
                patience_counter_metric += 1

            # Early stopping check
            if (patience_counter_loss >= patience) or (patience_counter_metric >= patience):
                print(f"Early stopping at epoch {epoch+1}")
                break


        # Print status
        if verbose:
          if epoch % 19 == 0:
            print(f"| Epoch {epoch+1} | Train Loss: {epoch_loss / len(train_loader):.4f}, Validation Loss: {loss_val:.4f} | Train Accuracy: {accuracy_train:.4f}, Val Accuracy: {accuracy_val:.4f} |")

    dropout_results.append({'dropout_prob': dropout_prob,
                                    'train_loss': train_losses,
                                    'val_loss': val_losses,
                                    'training_acc': train_accuracies,
                                    'validation_acc': val_accuracies
                                    })
    # Load the best model
    model.load_state_dict(best_model)
    print(f"Best validation accuracy: {best_eval_metric_val:.4f}")

    writer.close()
    return model

epochs=10000
learning_rate = 0.0001 # highest val_score at learning_rate 0.0001

dropout_probs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]


for dropout_prob in dropout_probs:
  model = GaitGenderClassifier(dropout_prob)
  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
  criterion = nn.BCEWithLogitsLoss()  # For binary classification without sigmoid in final layer
  writer = SummaryWriter()

  # Training the model
  trained_model = training(model=model,
                           X_train=data_train_tensor,
                           y_train=target_train_tensor,
                           X_val=data_val_tensor,
                           y_val=target_val_tensor,
                           criterion=criterion,
                           optimizer=optimizer,
                           epochs=epochs,
                           writer=writer,
                           dropout_prob=dropout_prob,
                           log_tensorboard=False,
                           log_dropout=True,
                           verbose=True)

| Epoch 1 | Train Loss: 0.6912, Validation Loss: 0.6820 | Train Accuracy: 0.6206, Val Accuracy: 0.6398 |
| Epoch 20 | Train Loss: 0.1779, Validation Loss: 0.2766 | Train Accuracy: 0.9673, Val Accuracy: 0.9092 |
| Epoch 39 | Train Loss: 0.0437, Validation Loss: 0.1847 | Train Accuracy: 0.9952, Val Accuracy: 0.9539 |
| Epoch 58 | Train Loss: 0.0169, Validation Loss: 0.2077 | Train Accuracy: 0.9986, Val Accuracy: 0.9539 |
| Epoch 77 | Train Loss: 0.0200, Validation Loss: 0.2091 | Train Accuracy: 0.9986, Val Accuracy: 0.9553 |
Early stopping at epoch 89
Best validation accuracy: 0.9597
| Epoch 1 | Train Loss: 0.7115, Validation Loss: 0.6877 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
| Epoch 20 | Train Loss: 0.3020, Validation Loss: 0.2905 | Train Accuracy: 0.9270, Val Accuracy: 0.8890 |
| Epoch 39 | Train Loss: 0.0353, Validation Loss: 0.1808 | Train Accuracy: 0.9976, Val Accuracy: 0.9640 |
| Epoch 58 | Train Loss: 0.0306, Validation Loss: 0.1657 | Train Accuracy: 0.9976, Val Accuracy: 0.9697 |
| Epoch 77 | Train Loss: 0.0317, Validation Loss: 0.1675 | Train Accuracy: 0.9976, Val Accuracy: 0.9697 |
Early stopping at epoch 82
Best validation accuracy: 0.9726
| Epoch 1 | Train Loss: 0.7351, Validation Loss: 0.6950 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 20 | Train Loss: 0.4086, Validation Loss: 0.3200 | Train Accuracy: 0.9092, Val Accuracy: 0.8775 |
| Epoch 39 | Train Loss: 0.0848, Validation Loss: 0.2648 | Train Accuracy: 0.9837, Val Accuracy: 0.9380 |
| Epoch 58 | Train Loss: 0.0352, Validation Loss: 0.2207 | Train Accuracy: 0.9976, Val Accuracy: 0.9524 |
| Epoch 77 | Train Loss: 0.0333, Validation Loss: 0.2316 | Train Accuracy: 0.9990, Val Accuracy: 0.9524 |
Early stopping at epoch 87
Best validation accuracy: 0.9568
| Epoch 1 | Train Loss: 0.7926, Validation Loss: 0.7107 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 20 | Train Loss: 0.6237, Validation Loss: 0.5537 | Train Accuracy: 0.8650, Val Accuracy: 0.8487 |
| Epoch 39 | Train Loss: 0.2363, Validation Loss: 0.1486 | Train Accuracy: 0.9731, Val Accuracy: 0.9568 |
| Epoch 58 | Train Loss: 0.0527, Validation Loss: 0.1223 | Train Accuracy: 0.9995, Val Accuracy: 0.9798 |
| Epoch 77 | Train Loss: 0.0501, Validation Loss: 0.1300 | Train Accuracy: 1.0000, Val Accuracy: 0.9827 |
| Epoch 96 | Train Loss: 0.0457, Validation Loss: 0.1314 | Train Accuracy: 1.0000, Val Accuracy: 0.9827 |
Early stopping at epoch 102
Best validation accuracy: 0.9827
| Epoch 1 | Train Loss: 0.7440, Validation Loss: 0.6920 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
| Epoch 20 | Train Loss: 0.6976, Validation Loss: 0.6998 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
| Epoch 39 | Train Loss: 0.6938, Validation Loss: 0.6996 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
Early stopping at epoch 51
Best validation accuracy: 0.5173
| Epoch 1 | Train Loss: 0.9118, Validation Loss: 0.7279 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 20 | Train Loss: 0.7098, Validation Loss: 0.6961 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 39 | Train Loss: 0.6993, Validation Loss: 0.6947 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
Early stopping at epoch 51
Best validation accuracy: 0.4827
| Epoch 1 | Train Loss: 1.1165, Validation Loss: 0.7016 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 20 | Train Loss: 0.7283, Validation Loss: 0.6966 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 39 | Train Loss: 0.7046, Validation Loss: 0.6953 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
Early stopping at epoch 51
Best validation accuracy: 0.4827
| Epoch 1 | Train Loss: 1.8160, Validation Loss: 0.6979 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
| Epoch 20 | Train Loss: 1.0527, Validation Loss: 0.6950 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
| Epoch 39 | Train Loss: 0.8591, Validation Loss: 0.6945 | Train Accuracy: 0.5173, Val Accuracy: 0.5173 |
Early stopping at epoch 51
Best validation accuracy: 0.5173

fig, axis = plt.subplots(1, 2, figsize=(15, 10))

# Define the color map
colors = cm.RdYlGn(np.linspace(0, 1, len(dropout_results)))

# Plotting the results with the Spectral color palette
for idx, result in enumerate(dropout_results):
    axis[0].plot(result['train_loss'], label=f"train drop={result['dropout_prob']}", linestyle='--', color=colors[idx])
    axis[0].plot(result['val_loss'], label=f"val drop={result['dropout_prob']}", color=colors[idx])
    axis[1].plot(result['validation_acc'], label=f"val drop={result['dropout_prob']}", color=colors[idx])
    axis[1].plot(result['training_acc'], label=f"train drop={result['dropout_prob']}", linestyle='--', color=colors[idx])

# Configure axis 0 (Loss plot)
axis[0].set_xlabel("Epoch")
axis[0].set_ylabel("Loss")
axis[0].set_title("Loss vs Dropout Probability")
axis[0].legend()

# Configure axis 1 (Accuracy plot)
axis[1].set_xlabel("Epoch")
axis[1].set_ylabel("Accuracy")
axis[1].set_title("Accuracy vs Dropout Probability")
axis[1].legend()

plt.show()

# %load_ext tensorboard

# %tensorboard --logdir runs

#%tensorboard --logdir=runs/ --host localhost --port 8088 # http://localhost:8088

dropout_prob = 0.4
# Instantiate model, criterion, optimizer, and TensorBoard writer
model = GaitGenderClassifier(dropout_prob)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.BCEWithLogitsLoss()  # For binary classification without sigmoid in final layer
writer = SummaryWriter()

# Training the model
trained_model = training(model=model,
                          X_train=data_train_tensor,
                          y_train=target_train_tensor,
                          X_val=data_val_tensor,
                          y_val=target_val_tensor,
                          criterion=criterion,
                          optimizer=optimizer,
                          epochs=epochs,
                          writer=writer,
                          dropout_prob=dropout_prob,
                          log_tensorboard=True,
                          log_dropout=False,
                          verbose=True)

| Epoch 1 | Train Loss: 0.7285, Validation Loss: 0.6999 | Train Accuracy: 0.4827, Val Accuracy: 0.4827 |
| Epoch 20 | Train Loss: 0.6001, Validation Loss: 0.5522 | Train Accuracy: 0.8622, Val Accuracy: 0.8401 |
| Epoch 39 | Train Loss: 0.2803, Validation Loss: 0.1905 | Train Accuracy: 0.9616, Val Accuracy: 0.9452 |
| Epoch 58 | Train Loss: 0.1748, Validation Loss: 0.1707 | Train Accuracy: 0.9798, Val Accuracy: 0.9539 |
| Epoch 77 | Train Loss: 0.1394, Validation Loss: 0.1614 | Train Accuracy: 0.9870, Val Accuracy: 0.9582 |
| Epoch 96 | Train Loss: 0.1163, Validation Loss: 0.1624 | Train Accuracy: 0.9870, Val Accuracy: 0.9582 |
Early stopping at epoch 111
Best validation accuracy: 0.9597

from PIL import Image
def plot_image(name):
    img = Image.open(name)
    plt.imshow(img)
    plt.axis('off')
    plt.show()

try:
    plot_image("/content/drive/MyDrive/HochschuleKiel/DeepLearning/Portfolio Exam/code/Tensorbaord - Loss.png")
except:
    plot_image("Tensorbaord - Loss.png")

try:
    plot_image("/content/drive/MyDrive/HochschuleKiel/DeepLearning/Portfolio Exam/code/Tensorboard - Accuracy.png")
except:
    plot_image("Tensorboard - Accuracy.png")

# bring tensor back to cpu
data_train_tensor = data_train_tensor.to('cpu')
data_test_tensor = data_test_tensor.to('cpu')
data_val_tensor = data_val_tensor.to('cpu')

target_train_tensor = target_train_tensor.to('cpu')
target_test_tensor = target_test_tensor.to('cpu')
target_val_tensor = target_val_tensor.to('cpu')

# also the model
model.to('cpu');

results = add_results(results, "Deep Learning",
                      target_train_tensor,
                      model.predict(*get_data(data_train_tensor)),
                      target_val_tensor,
                      model.predict(*get_data(data_val_tensor)),
                      target_test_tensor,
                      model.predict(*get_data(data_test_tensor)))
display(results)

	age	subject	sex	created	cycle	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
0	19	sub-002	male	2024-05-29 12:27:14	cycle_1	[-2.768442, -3.496685, -4.069201, -4.462231, -...	[7.935368, 8.292075, 8.685886, 9.081171, 9.438...	[0.6022689999999999, 0.629934, 0.660829, 0.667...	[0.0, -0.002226, -0.00464, -0.0072429999999999...	[0.0, -0.000268, -0.00040699999999999997, -0.0...	[0.0, -0.001321, -0.0026279999999999997, -0.00...	[22.490803, 22.188433, 21.868492, 21.554268, 2...	[-0.010251999999999999, -0.01488, -0.017207, -...	[104.20636, 103.466881, 102.595413, 101.67038,...	[14.214731, 13.85244, 13.169363, 12.26093, 11....	[-3.179904, -3.617692, -4.20662, -4.875635, -5...	[19.032904, 18.2679, 17.341208, 16.317291, 15....	[14.214735, 13.852443, 13.169367, 12.260934, 1...	[-3.179903, -3.61769, -4.206618, -4.875634, -5...	[-19.032904, -18.2679, -17.341208, -16.317291,...	[20.6766, 20.222738, 19.856031, 19.556093, 19....	[-4.393382, -4.227548, -4.138162, -4.11265, -4...	[-0.280891, -1.543496, -2.484963, -3.042148, -...	[2.135388, 1.461672, 1.318257, 1.61889, 2.2538...	[-0.194929, -0.818533, -1.206393, -1.397462, -...	[-14.350356, -12.79824, -11.413175, -10.301519...	[3.991429, 3.891927, 3.795211, 3.698814, 3.599...	[-0.522435, -0.717429, -0.9033939999999999, -1...	[-4.647504, -4.119165, -3.555717, -2.977560999...	[2.708186, 2.7229010000000002, 2.779783, 2.872...	[-8.669715, -8.55989, -8.467485, -8.390489, -8...	[16.503153, 16.36441, 16.326563, 16.373936, 16...	[3.141546, 3.062634, 2.954683, 2.827985, 2.697...	[-1.945191, -1.821488, -1.716818, -1.627697, -...	[-2.919237, -3.102238, -3.3029479999999998, -3...	[7.254066, 7.08643, 6.894266, 6.684382, 6.4677...	[-2.086775, -2.146077, -2.215677, -2.286167, -...	[-7.7877279999999995, -7.421407, -7.040127, -6...	[-5.4825289999999995, -5.833911, -6.063342, -6...	[4.380441, 4.43795, 4.501745, 4.57004, 4.64473...	[2.074525, 3.183789, 4.164791, 4.954755, 5.505...	[0.0, 0.0015400000000000001, 0.003264, 0.00518...	[0.0, 0.000522, 0.001184, 0.001996, 0.002956, ...	[0.0, -0.000998, -0.0020039999999999997, -0.00...	[28.822729, 28.978001, 29.148277, 29.292614, 2...	[0.003681, 0.007298, 0.009037, 0.008681, 0.007...	[97.213318, 96.508972, 95.770767, 95.081993, 9...	[10.846501, 9.729046, 8.539912, 7.347647, 6.20...	[4.045348, 4.578834, 5.088349, 5.536511, 5.904...	[-13.617139, -12.353103, -10.997869, -9.585029...	[10.846505, 9.72905, 8.539916999999999, 7.3476...	[4.045347, 4.578834, 5.088348, 5.53651, 5.9045...	[-13.617139, -12.353103, -10.997869, -9.585029...	[18.893475, 19.141722, 19.27253, 19.289482, 19...	[-2.407445, -2.328364, -2.257316, -2.183507, -...	[-3.71332, -3.729039, -3.720179, -3.685369, -3...	[3.157154, 3.974303, 4.884859, 5.819755, 6.725...	[-0.8432679999999999, -0.736147, -0.5383439999...	[-15.543177, -15.934821, -16.170027, -16.20980...	[0.883767, 1.096725, 1.264113, 1.393558, 1.498...	[-0.365954, -0.43199099999999996, -0.456648999...	[4.044459, 4.620235, 5.210877, 5.8096049999999...	[-5.663974, -6.074879, -6.471653, -6.800329, -...	[-10.194664, -10.122283, -10.048591, -9.980521...	[8.034688, 7.914454, 7.848456, 7.869713, 7.992...	[7.059786, 6.799669, 6.570794, 6.367293, 6.177...	[-0.765127, -0.755408, -0.785045, -0.849924999...	[-4.957033, -5.034188, -5.127676, -5.235561, -...	[8.011764, 7.970358, 7.914809, 7.84646, 7.7663...	[-0.435287, -0.484906, -0.531291, -0.570133, -...	[-0.9707739999999999, -0.470057, 0.02395599999...
1	19	sub-002	male	2024-05-29 12:27:14	cycle_10	[-7.611665, -8.296784, -8.636337, -8.653679, -...	[6.345243, 6.41904, 6.53749, 6.682159, 6.82977...	[0.015241, 1.130675, 2.046575, 2.656725, 2.912...	[0.016838, 0.015413, 0.013836999999999999, 0.0...	[-0.009348, -0.009233, -0.008978, -0.008558, -...	[0.000483, -0.001058, -0.002572, -0.004029, -0...	[22.925438, 22.503048, 22.122334, 21.826393, 2...	[-0.019576, -0.016118999999999998, -0.01080699...	[102.04879, 101.1464, 100.409393, 99.857368, 9...	[12.341584, 12.335873, 12.083894, 11.615406, 1...	[-5.800112, -5.918781, -6.172589, -6.485641, -...	[26.423983, 25.275766, 23.834427, 22.164652, 2...	[12.341588, 12.335876, 12.083896, 11.615409, 1...	[-5.80011, -5.918779, -6.172588, -6.485639, -6...	[-26.423983, -25.275766, -23.834427, -22.16465...	[20.424934, 19.962803, 19.509947, 19.021643, 1...	[-1.797753, -1.688857, -1.708179, -1.814137999...	[-2.878228, -2.440343, -1.9427249999999998, -1...	[2.762847, 1.953323, 1.724262, 1.937817, 2.447...	[-0.236487, -0.9723259999999999, -1.39173, -1....	[-18.446913, -19.233843, -19.68598, -19.714357...	[1.090251, 0.818932, 0.5202, 0.200094, -0.1280...	[-0.028227, -0.38070299999999996, -0.728997, -...	[-4.88499, -4.3810839999999995, -3.769862, -3....	[3.134768, 3.105432, 3.044675, 2.940096, 2.789...	[-9.577947, -9.449483, -9.323687, -9.206041, -...	[18.54364, 18.746981, 18.860155, 18.887785, 18...	[4.173757, 4.379654, 4.612041, 4.860198, 5.105...	[-1.197732, -0.843793, -0.5067539999999999, -0...	[-2.178959, -2.3056609999999997, -2.478065, -2...	[5.30765, 5.244393, 5.181376, 5.113739, 5.0358...	[-1.020125, -1.011145, -1.010935, -1.014616, -...	[-7.164998, -6.754079, -6.28371, -5.74174, -5....	[-5.628355, -5.81796, -5.811576, -5.627428, -5...	[3.403195, 3.262351, 3.242479, 3.355409, 3.601...	[-3.4450979999999998, -2.903352, -2.27135, -1....	[-0.008744, -0.007463, -0.006006, -0.004360999...	[-0.012428999999999999, -0.01247, -0.012466, -...	[0.002436, 0.001673, 0.000812, -0.000139, -0.0...	[31.828133, 31.580637, 31.387247, 31.242411, 3...	[-0.0011359999999999999, 0.00102, 0.001377, 0....	[90.242859, 90.552948, 91.0634, 91.718605, 92....	[9.194756, 8.696275, 8.075597, 7.379229, 6.644...	[4.146616, 4.320601, 4.622187, 5.041539, 5.558...	[-21.498556, -20.357746, -19.085051, -17.74668...	[9.194759, 8.696277, 8.075601, 7.379233, 6.644...	[4.146615, 4.3206, 4.622186, 5.041538, 5.55823...	[-21.498556, -20.357746, -19.085051, -17.74668...	[18.847454, 18.874477, 18.864149, 18.821054, 1...	[-5.771685, -5.643616, -5.501174, -5.346303, -...	[-7.284528, -7.00043, -6.623891, -6.122418, -5...	[7.355994, 7.557222, 8.092501, 8.881246, 9.836...	[2.684278, 2.574164, 2.540403, 2.580063, 2.684...	[-10.199863, -10.37021, -10.588555, -10.881887...	[-1.597058, -1.574028, -1.5760939999999999, -1...	[-1.239754, -1.412793, -1.553985, -1.665565, -...	[0.427455, 0.937423, 1.446885, 1.9577300000000...	[-3.041614, -3.081234, -3.148071, -3.257658, -...	[-10.598993, -10.551416, -10.489378, -10.40383...	[10.954481, 10.959135, 10.947763, 10.889692, 1...	[8.854305, 8.837968, 8.840726, 8.842192, 8.807...	[-1.246204, -1.063445, -0.88775, -0.716517, -0...	[-5.760704, -5.845308, -5.895671, -5.918782, -...	[7.472655, 7.480461, 7.478623, 7.462115, 7.425...	[-1.736641, -1.716788, -1.6773069999999999, -1...	[-5.447077, -4.997944, -4.516113, -4.006367, -...
2	19	sub-002	male	2024-05-29 12:27:14	cycle_11	[-4.634666, -5.02648, -5.340784, -5.544887, -5...	[7.019156, 7.207421, 7.360003, 7.428865, 7.382...	[0.8590829999999999, 0.9442879999999999, 1.291...	[0.020581, 0.018594, 0.016465, 0.014183, 0.011...	[-0.041350000000000005, -0.04147000000000001, ...	[-0.002153, -0.003436, -0.004659, -0.005737, -...	[21.892666, 21.667431, 21.510859, 21.407866, 2...	[0.006052, 0.0068579999999999995, 0.0050479999...	[103.10041, 103.414948, 103.769325, 104.036888...	[13.801257, 13.364927, 12.614091, 11.681816, 1...	[-7.515644, -7.981946, -8.420057, -8.740601999...	[19.812559, 19.102261, 18.223965, 17.228157, 1...	[13.801261, 13.36493, 12.614095, 11.681819, 10...	[-7.515642, -7.981944, -8.420057, -8.740601999...	[-19.812559, -19.102261, -18.223965, -17.22815...	[21.490784, 21.206192, 20.810915, 20.239679, 1...	[-1.003782, -1.046244, -1.167168, -1.336805, -...	[1.397087, 2.274668, 2.839696, 3.0362, 2.89127...	[4.38941, 4.258365, 4.485112, 4.878491, 5.2717...	[1.547475, 1.425399, 1.3594089999999999, 1.309...	[-18.630737, -19.466963, -20.113201, -20.44350...	[1.38354, 1.262527, 1.063718, 0.784248, 0.4453...	[0.217076, -0.012333, -0.265026, -0.52868, -0....	[-3.368124, -2.697494, -1.98144, -1.229187, -0...	[1.134091, 0.990254, 0.783542, 0.540679, 0.294...	[-10.493185, -10.305758, -10.112445, -9.931076...	[17.463486, 17.312468, 17.166122, 17.092224, 1...	[3.597836, 3.621159, 3.705733, 3.861809, 4.075...	[-1.154968, -0.9766429999999999, -0.759751, -0...	[-2.844895, -2.9492849999999997, -3.022531, -3...	[5.022056, 4.928493, 4.817266, 4.69589, 4.5721...	[-0.685429, -0.7331249999999999, -0.7694989999...	[-6.30276, -5.719454, -5.056945, -4.33003, -3....	[-6.689217, -7.057434, -7.268858, -7.303356, -...	[4.002138, 4.135075, 4.315419, 4.513762, 4.705...	[-1.061941, -0.874251, -0.6129100000000001, -0...	[-0.010591, -0.008883, -0.006986999999999999, ...	[-0.013071, -0.014, -0.014787, -0.015403, -0.0...	[0.002051, 0.001292, 0.0005059999999999999, -0...	[33.081654, 32.856186, 32.723984, 32.691216, 3...	[0.005253, 0.00417, 0.002565, 0.001298, 0.0009...	[92.021736, 92.243813, 92.391449, 92.442314, 9...	[8.570474, 7.685758, 6.655786, 5.567538, 4.500...	[4.459383, 4.893425, 5.360785, 5.814991, 6.221...	[-14.959339, -13.506869, -12.001929, -10.49032...	[8.570477, 7.685762, 6.65579, 5.56754199999999...	[4.459382, 4.893425, 5.360784, 5.81499, 6.2219...	[-14.959339, -13.506869, -12.001929, -10.49032...	[16.00716, 16.189598, 16.405577, 16.658844, 16...	[-2.608135, -2.575316, -2.595477, -2.666513, -...	[-5.038992, -5.088731, -5.152913, -5.168307, -...	[3.29341, 3.71739, 4.438928, 5.395056, 6.51829...	[0.818074, 0.758255, 0.774924, 0.854435, 0.989...	[-9.693586, -9.014916, -8.393165, -7.926613, -...	[-1.286025, -1.090904, -0.854332, -0.579944999...	[-0.204015, -0.38847899999999996, -0.562819, -...	[1.125047, 1.8016709999999998, 2.525921, 3.273...	[-4.353323, -4.315862, -4.385724, -4.564363, -...	[-10.74014, -10.643498, -10.525556, -10.392888...	[13.31672, 13.247898, 13.045144, 12.724239, 12...	[8.859, 8.63991, 8.336942, 7.95187, 7.500204, ...	[0.103183, 0.21866000000000002, 0.307375, 0.37...	[-5.57998, -5.7986889999999995, -6.033959, -6....	[7.547276, 7.527881, 7.468235, 7.366483, 7.225...	[0.632976, 0.589656, 0.5276609999999999, 0.448...	[-4.3928519999999995, -3.91817, -3.416698, -2....

	age	subject	sex	created	cycle	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
0	19	sub-002	male	2024-05-29 12:27:14	cycle_1	[-2.768442, -3.496685, -4.069201, -4.462231, -...	[7.935368, 8.292075, 8.685886, 9.081171, 9.438...	[0.6022689999999999, 0.629934, 0.660829, 0.667...	[0.0, -0.002226, -0.00464, -0.0072429999999999...	[0.0, -0.000268, -0.00040699999999999997, -0.0...	[0.0, -0.001321, -0.0026279999999999997, -0.00...	[22.490803, 22.188433, 21.868492, 21.554268, 2...	[-0.010251999999999999, -0.01488, -0.017207, -...	[104.20636, 103.466881, 102.595413, 101.67038,...	[14.214731, 13.85244, 13.169363, 12.26093, 11....	[-3.179904, -3.617692, -4.20662, -4.875635, -5...	[19.032904, 18.2679, 17.341208, 16.317291, 15....	[14.214735, 13.852443, 13.169367, 12.260934, 1...	[-3.179903, -3.61769, -4.206618, -4.875634, -5...	[-19.032904, -18.2679, -17.341208, -16.317291,...	[20.6766, 20.222738, 19.856031, 19.556093, 19....	[-4.393382, -4.227548, -4.138162, -4.11265, -4...	[-0.280891, -1.543496, -2.484963, -3.042148, -...	[2.135388, 1.461672, 1.318257, 1.61889, 2.2538...	[-0.194929, -0.818533, -1.206393, -1.397462, -...	[-14.350356, -12.79824, -11.413175, -10.301519...	[3.991429, 3.891927, 3.795211, 3.698814, 3.599...	[-0.522435, -0.717429, -0.9033939999999999, -1...	[-4.647504, -4.119165, -3.555717, -2.977560999...	[2.708186, 2.7229010000000002, 2.779783, 2.872...	[-8.669715, -8.55989, -8.467485, -8.390489, -8...	[16.503153, 16.36441, 16.326563, 16.373936, 16...	[3.141546, 3.062634, 2.954683, 2.827985, 2.697...	[-1.945191, -1.821488, -1.716818, -1.627697, -...	[-2.919237, -3.102238, -3.3029479999999998, -3...	[7.254066, 7.08643, 6.894266, 6.684382, 6.4677...	[-2.086775, -2.146077, -2.215677, -2.286167, -...	[-7.7877279999999995, -7.421407, -7.040127, -6...	[-5.4825289999999995, -5.833911, -6.063342, -6...	[4.380441, 4.43795, 4.501745, 4.57004, 4.64473...	[2.074525, 3.183789, 4.164791, 4.954755, 5.505...	[0.0, 0.0015400000000000001, 0.003264, 0.00518...	[0.0, 0.000522, 0.001184, 0.001996, 0.002956, ...	[0.0, -0.000998, -0.0020039999999999997, -0.00...	[28.822729, 28.978001, 29.148277, 29.292614, 2...	[0.003681, 0.007298, 0.009037, 0.008681, 0.007...	[97.213318, 96.508972, 95.770767, 95.081993, 9...	[10.846501, 9.729046, 8.539912, 7.347647, 6.20...	[4.045348, 4.578834, 5.088349, 5.536511, 5.904...	[-13.617139, -12.353103, -10.997869, -9.585029...	[10.846505, 9.72905, 8.539916999999999, 7.3476...	[4.045347, 4.578834, 5.088348, 5.53651, 5.9045...	[-13.617139, -12.353103, -10.997869, -9.585029...	[18.893475, 19.141722, 19.27253, 19.289482, 19...	[-2.407445, -2.328364, -2.257316, -2.183507, -...	[-3.71332, -3.729039, -3.720179, -3.685369, -3...	[3.157154, 3.974303, 4.884859, 5.819755, 6.725...	[-0.8432679999999999, -0.736147, -0.5383439999...	[-15.543177, -15.934821, -16.170027, -16.20980...	[0.883767, 1.096725, 1.264113, 1.393558, 1.498...	[-0.365954, -0.43199099999999996, -0.456648999...	[4.044459, 4.620235, 5.210877, 5.8096049999999...	[-5.663974, -6.074879, -6.471653, -6.800329, -...	[-10.194664, -10.122283, -10.048591, -9.980521...	[8.034688, 7.914454, 7.848456, 7.869713, 7.992...	[7.059786, 6.799669, 6.570794, 6.367293, 6.177...	[-0.765127, -0.755408, -0.785045, -0.849924999...	[-4.957033, -5.034188, -5.127676, -5.235561, -...	[8.011764, 7.970358, 7.914809, 7.84646, 7.7663...	[-0.435287, -0.484906, -0.531291, -0.570133, -...	[-0.9707739999999999, -0.470057, 0.02395599999...
1	19	sub-002	male	2024-05-29 12:27:14	cycle_10	[-7.611665, -8.296784, -8.636337, -8.653679, -...	[6.345243, 6.41904, 6.53749, 6.682159, 6.82977...	[0.015241, 1.130675, 2.046575, 2.656725, 2.912...	[0.016838, 0.015413, 0.013836999999999999, 0.0...	[-0.009348, -0.009233, -0.008978, -0.008558, -...	[0.000483, -0.001058, -0.002572, -0.004029, -0...	[22.925438, 22.503048, 22.122334, 21.826393, 2...	[-0.019576, -0.016118999999999998, -0.01080699...	[102.04879, 101.1464, 100.409393, 99.857368, 9...	[12.341584, 12.335873, 12.083894, 11.615406, 1...	[-5.800112, -5.918781, -6.172589, -6.485641, -...	[26.423983, 25.275766, 23.834427, 22.164652, 2...	[12.341588, 12.335876, 12.083896, 11.615409, 1...	[-5.80011, -5.918779, -6.172588, -6.485639, -6...	[-26.423983, -25.275766, -23.834427, -22.16465...	[20.424934, 19.962803, 19.509947, 19.021643, 1...	[-1.797753, -1.688857, -1.708179, -1.814137999...	[-2.878228, -2.440343, -1.9427249999999998, -1...	[2.762847, 1.953323, 1.724262, 1.937817, 2.447...	[-0.236487, -0.9723259999999999, -1.39173, -1....	[-18.446913, -19.233843, -19.68598, -19.714357...	[1.090251, 0.818932, 0.5202, 0.200094, -0.1280...	[-0.028227, -0.38070299999999996, -0.728997, -...	[-4.88499, -4.3810839999999995, -3.769862, -3....	[3.134768, 3.105432, 3.044675, 2.940096, 2.789...	[-9.577947, -9.449483, -9.323687, -9.206041, -...	[18.54364, 18.746981, 18.860155, 18.887785, 18...	[4.173757, 4.379654, 4.612041, 4.860198, 5.105...	[-1.197732, -0.843793, -0.5067539999999999, -0...	[-2.178959, -2.3056609999999997, -2.478065, -2...	[5.30765, 5.244393, 5.181376, 5.113739, 5.0358...	[-1.020125, -1.011145, -1.010935, -1.014616, -...	[-7.164998, -6.754079, -6.28371, -5.74174, -5....	[-5.628355, -5.81796, -5.811576, -5.627428, -5...	[3.403195, 3.262351, 3.242479, 3.355409, 3.601...	[-3.4450979999999998, -2.903352, -2.27135, -1....	[-0.008744, -0.007463, -0.006006, -0.004360999...	[-0.012428999999999999, -0.01247, -0.012466, -...	[0.002436, 0.001673, 0.000812, -0.000139, -0.0...	[31.828133, 31.580637, 31.387247, 31.242411, 3...	[-0.0011359999999999999, 0.00102, 0.001377, 0....	[90.242859, 90.552948, 91.0634, 91.718605, 92....	[9.194756, 8.696275, 8.075597, 7.379229, 6.644...	[4.146616, 4.320601, 4.622187, 5.041539, 5.558...	[-21.498556, -20.357746, -19.085051, -17.74668...	[9.194759, 8.696277, 8.075601, 7.379233, 6.644...	[4.146615, 4.3206, 4.622186, 5.041538, 5.55823...	[-21.498556, -20.357746, -19.085051, -17.74668...	[18.847454, 18.874477, 18.864149, 18.821054, 1...	[-5.771685, -5.643616, -5.501174, -5.346303, -...	[-7.284528, -7.00043, -6.623891, -6.122418, -5...	[7.355994, 7.557222, 8.092501, 8.881246, 9.836...	[2.684278, 2.574164, 2.540403, 2.580063, 2.684...	[-10.199863, -10.37021, -10.588555, -10.881887...	[-1.597058, -1.574028, -1.5760939999999999, -1...	[-1.239754, -1.412793, -1.553985, -1.665565, -...	[0.427455, 0.937423, 1.446885, 1.9577300000000...	[-3.041614, -3.081234, -3.148071, -3.257658, -...	[-10.598993, -10.551416, -10.489378, -10.40383...	[10.954481, 10.959135, 10.947763, 10.889692, 1...	[8.854305, 8.837968, 8.840726, 8.842192, 8.807...	[-1.246204, -1.063445, -0.88775, -0.716517, -0...	[-5.760704, -5.845308, -5.895671, -5.918782, -...	[7.472655, 7.480461, 7.478623, 7.462115, 7.425...	[-1.736641, -1.716788, -1.6773069999999999, -1...	[-5.447077, -4.997944, -4.516113, -4.006367, -...

	session_name	age	sex	cycle	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
0	sub-002_2024-05-29 12:27:14	19	male	cycle_1	[-2.768442, -3.496685, -4.069201, -4.462231, -...	[7.935368, 8.292075, 8.685886, 9.081171, 9.438...	[0.6022689999999999, 0.629934, 0.660829, 0.667...	[0.0, -0.002226, -0.00464, -0.0072429999999999...	[0.0, -0.000268, -0.00040699999999999997, -0.0...	[0.0, -0.001321, -0.0026279999999999997, -0.00...	[22.490803, 22.188433, 21.868492, 21.554268, 2...	[-0.010251999999999999, -0.01488, -0.017207, -...	[104.20636, 103.466881, 102.595413, 101.67038,...	[14.214731, 13.85244, 13.169363, 12.26093, 11....	[-3.179904, -3.617692, -4.20662, -4.875635, -5...	[19.032904, 18.2679, 17.341208, 16.317291, 15....	[14.214735, 13.852443, 13.169367, 12.260934, 1...	[-3.179903, -3.61769, -4.206618, -4.875634, -5...	[-19.032904, -18.2679, -17.341208, -16.317291,...	[20.6766, 20.222738, 19.856031, 19.556093, 19....	[-4.393382, -4.227548, -4.138162, -4.11265, -4...	[-0.280891, -1.543496, -2.484963, -3.042148, -...	[2.135388, 1.461672, 1.318257, 1.61889, 2.2538...	[-0.194929, -0.818533, -1.206393, -1.397462, -...	[-14.350356, -12.79824, -11.413175, -10.301519...	[3.991429, 3.891927, 3.795211, 3.698814, 3.599...	[-0.522435, -0.717429, -0.9033939999999999, -1...	[-4.647504, -4.119165, -3.555717, -2.977560999...	[2.708186, 2.7229010000000002, 2.779783, 2.872...	[-8.669715, -8.55989, -8.467485, -8.390489, -8...	[16.503153, 16.36441, 16.326563, 16.373936, 16...	[3.141546, 3.062634, 2.954683, 2.827985, 2.697...	[-1.945191, -1.821488, -1.716818, -1.627697, -...	[-2.919237, -3.102238, -3.3029479999999998, -3...	[7.254066, 7.08643, 6.894266, 6.684382, 6.4677...	[-2.086775, -2.146077, -2.215677, -2.286167, -...	[-7.7877279999999995, -7.421407, -7.040127, -6...	[-5.4825289999999995, -5.833911, -6.063342, -6...	[4.380441, 4.43795, 4.501745, 4.57004, 4.64473...	[2.074525, 3.183789, 4.164791, 4.954755, 5.505...	[0.0, 0.0015400000000000001, 0.003264, 0.00518...	[0.0, 0.000522, 0.001184, 0.001996, 0.002956, ...	[0.0, -0.000998, -0.0020039999999999997, -0.00...	[28.822729, 28.978001, 29.148277, 29.292614, 2...	[0.003681, 0.007298, 0.009037, 0.008681, 0.007...	[97.213318, 96.508972, 95.770767, 95.081993, 9...	[10.846501, 9.729046, 8.539912, 7.347647, 6.20...	[4.045348, 4.578834, 5.088349, 5.536511, 5.904...	[-13.617139, -12.353103, -10.997869, -9.585029...	[10.846505, 9.72905, 8.539916999999999, 7.3476...	[4.045347, 4.578834, 5.088348, 5.53651, 5.9045...	[-13.617139, -12.353103, -10.997869, -9.585029...	[18.893475, 19.141722, 19.27253, 19.289482, 19...	[-2.407445, -2.328364, -2.257316, -2.183507, -...	[-3.71332, -3.729039, -3.720179, -3.685369, -3...	[3.157154, 3.974303, 4.884859, 5.819755, 6.725...	[-0.8432679999999999, -0.736147, -0.5383439999...	[-15.543177, -15.934821, -16.170027, -16.20980...	[0.883767, 1.096725, 1.264113, 1.393558, 1.498...	[-0.365954, -0.43199099999999996, -0.456648999...	[4.044459, 4.620235, 5.210877, 5.8096049999999...	[-5.663974, -6.074879, -6.471653, -6.800329, -...	[-10.194664, -10.122283, -10.048591, -9.980521...	[8.034688, 7.914454, 7.848456, 7.869713, 7.992...	[7.059786, 6.799669, 6.570794, 6.367293, 6.177...	[-0.765127, -0.755408, -0.785045, -0.849924999...	[-4.957033, -5.034188, -5.127676, -5.235561, -...	[8.011764, 7.970358, 7.914809, 7.84646, 7.7663...	[-0.435287, -0.484906, -0.531291, -0.570133, -...	[-0.9707739999999999, -0.470057, 0.02395599999...
1	sub-002_2024-05-29 12:27:14	19	male	cycle_10	[-7.611665, -8.296784, -8.636337, -8.653679, -...	[6.345243, 6.41904, 6.53749, 6.682159, 6.82977...	[0.015241, 1.130675, 2.046575, 2.656725, 2.912...	[0.016838, 0.015413, 0.013836999999999999, 0.0...	[-0.009348, -0.009233, -0.008978, -0.008558, -...	[0.000483, -0.001058, -0.002572, -0.004029, -0...	[22.925438, 22.503048, 22.122334, 21.826393, 2...	[-0.019576, -0.016118999999999998, -0.01080699...	[102.04879, 101.1464, 100.409393, 99.857368, 9...	[12.341584, 12.335873, 12.083894, 11.615406, 1...	[-5.800112, -5.918781, -6.172589, -6.485641, -...	[26.423983, 25.275766, 23.834427, 22.164652, 2...	[12.341588, 12.335876, 12.083896, 11.615409, 1...	[-5.80011, -5.918779, -6.172588, -6.485639, -6...	[-26.423983, -25.275766, -23.834427, -22.16465...	[20.424934, 19.962803, 19.509947, 19.021643, 1...	[-1.797753, -1.688857, -1.708179, -1.814137999...	[-2.878228, -2.440343, -1.9427249999999998, -1...	[2.762847, 1.953323, 1.724262, 1.937817, 2.447...	[-0.236487, -0.9723259999999999, -1.39173, -1....	[-18.446913, -19.233843, -19.68598, -19.714357...	[1.090251, 0.818932, 0.5202, 0.200094, -0.1280...	[-0.028227, -0.38070299999999996, -0.728997, -...	[-4.88499, -4.3810839999999995, -3.769862, -3....	[3.134768, 3.105432, 3.044675, 2.940096, 2.789...	[-9.577947, -9.449483, -9.323687, -9.206041, -...	[18.54364, 18.746981, 18.860155, 18.887785, 18...	[4.173757, 4.379654, 4.612041, 4.860198, 5.105...	[-1.197732, -0.843793, -0.5067539999999999, -0...	[-2.178959, -2.3056609999999997, -2.478065, -2...	[5.30765, 5.244393, 5.181376, 5.113739, 5.0358...	[-1.020125, -1.011145, -1.010935, -1.014616, -...	[-7.164998, -6.754079, -6.28371, -5.74174, -5....	[-5.628355, -5.81796, -5.811576, -5.627428, -5...	[3.403195, 3.262351, 3.242479, 3.355409, 3.601...	[-3.4450979999999998, -2.903352, -2.27135, -1....	[-0.008744, -0.007463, -0.006006, -0.004360999...	[-0.012428999999999999, -0.01247, -0.012466, -...	[0.002436, 0.001673, 0.000812, -0.000139, -0.0...	[31.828133, 31.580637, 31.387247, 31.242411, 3...	[-0.0011359999999999999, 0.00102, 0.001377, 0....	[90.242859, 90.552948, 91.0634, 91.718605, 92....	[9.194756, 8.696275, 8.075597, 7.379229, 6.644...	[4.146616, 4.320601, 4.622187, 5.041539, 5.558...	[-21.498556, -20.357746, -19.085051, -17.74668...	[9.194759, 8.696277, 8.075601, 7.379233, 6.644...	[4.146615, 4.3206, 4.622186, 5.041538, 5.55823...	[-21.498556, -20.357746, -19.085051, -17.74668...	[18.847454, 18.874477, 18.864149, 18.821054, 1...	[-5.771685, -5.643616, -5.501174, -5.346303, -...	[-7.284528, -7.00043, -6.623891, -6.122418, -5...	[7.355994, 7.557222, 8.092501, 8.881246, 9.836...	[2.684278, 2.574164, 2.540403, 2.580063, 2.684...	[-10.199863, -10.37021, -10.588555, -10.881887...	[-1.597058, -1.574028, -1.5760939999999999, -1...	[-1.239754, -1.412793, -1.553985, -1.665565, -...	[0.427455, 0.937423, 1.446885, 1.9577300000000...	[-3.041614, -3.081234, -3.148071, -3.257658, -...	[-10.598993, -10.551416, -10.489378, -10.40383...	[10.954481, 10.959135, 10.947763, 10.889692, 1...	[8.854305, 8.837968, 8.840726, 8.842192, 8.807...	[-1.246204, -1.063445, -0.88775, -0.716517, -0...	[-5.760704, -5.845308, -5.895671, -5.918782, -...	[7.472655, 7.480461, 7.478623, 7.462115, 7.425...	[-1.736641, -1.716788, -1.6773069999999999, -1...	[-5.447077, -4.997944, -4.516113, -4.006367, -...
2	sub-002_2024-05-29 12:27:14	19	male	cycle_11	[-4.634666, -5.02648, -5.340784, -5.544887, -5...	[7.019156, 7.207421, 7.360003, 7.428865, 7.382...	[0.8590829999999999, 0.9442879999999999, 1.291...	[0.020581, 0.018594, 0.016465, 0.014183, 0.011...	[-0.041350000000000005, -0.04147000000000001, ...	[-0.002153, -0.003436, -0.004659, -0.005737, -...	[21.892666, 21.667431, 21.510859, 21.407866, 2...	[0.006052, 0.0068579999999999995, 0.0050479999...	[103.10041, 103.414948, 103.769325, 104.036888...	[13.801257, 13.364927, 12.614091, 11.681816, 1...	[-7.515644, -7.981946, -8.420057, -8.740601999...	[19.812559, 19.102261, 18.223965, 17.228157, 1...	[13.801261, 13.36493, 12.614095, 11.681819, 10...	[-7.515642, -7.981944, -8.420057, -8.740601999...	[-19.812559, -19.102261, -18.223965, -17.22815...	[21.490784, 21.206192, 20.810915, 20.239679, 1...	[-1.003782, -1.046244, -1.167168, -1.336805, -...	[1.397087, 2.274668, 2.839696, 3.0362, 2.89127...	[4.38941, 4.258365, 4.485112, 4.878491, 5.2717...	[1.547475, 1.425399, 1.3594089999999999, 1.309...	[-18.630737, -19.466963, -20.113201, -20.44350...	[1.38354, 1.262527, 1.063718, 0.784248, 0.4453...	[0.217076, -0.012333, -0.265026, -0.52868, -0....	[-3.368124, -2.697494, -1.98144, -1.229187, -0...	[1.134091, 0.990254, 0.783542, 0.540679, 0.294...	[-10.493185, -10.305758, -10.112445, -9.931076...	[17.463486, 17.312468, 17.166122, 17.092224, 1...	[3.597836, 3.621159, 3.705733, 3.861809, 4.075...	[-1.154968, -0.9766429999999999, -0.759751, -0...	[-2.844895, -2.9492849999999997, -3.022531, -3...	[5.022056, 4.928493, 4.817266, 4.69589, 4.5721...	[-0.685429, -0.7331249999999999, -0.7694989999...	[-6.30276, -5.719454, -5.056945, -4.33003, -3....	[-6.689217, -7.057434, -7.268858, -7.303356, -...	[4.002138, 4.135075, 4.315419, 4.513762, 4.705...	[-1.061941, -0.874251, -0.6129100000000001, -0...	[-0.010591, -0.008883, -0.006986999999999999, ...	[-0.013071, -0.014, -0.014787, -0.015403, -0.0...	[0.002051, 0.001292, 0.0005059999999999999, -0...	[33.081654, 32.856186, 32.723984, 32.691216, 3...	[0.005253, 0.00417, 0.002565, 0.001298, 0.0009...	[92.021736, 92.243813, 92.391449, 92.442314, 9...	[8.570474, 7.685758, 6.655786, 5.567538, 4.500...	[4.459383, 4.893425, 5.360785, 5.814991, 6.221...	[-14.959339, -13.506869, -12.001929, -10.49032...	[8.570477, 7.685762, 6.65579, 5.56754199999999...	[4.459382, 4.893425, 5.360784, 5.81499, 6.2219...	[-14.959339, -13.506869, -12.001929, -10.49032...	[16.00716, 16.189598, 16.405577, 16.658844, 16...	[-2.608135, -2.575316, -2.595477, -2.666513, -...	[-5.038992, -5.088731, -5.152913, -5.168307, -...	[3.29341, 3.71739, 4.438928, 5.395056, 6.51829...	[0.818074, 0.758255, 0.774924, 0.854435, 0.989...	[-9.693586, -9.014916, -8.393165, -7.926613, -...	[-1.286025, -1.090904, -0.854332, -0.579944999...	[-0.204015, -0.38847899999999996, -0.562819, -...	[1.125047, 1.8016709999999998, 2.525921, 3.273...	[-4.353323, -4.315862, -4.385724, -4.564363, -...	[-10.74014, -10.643498, -10.525556, -10.392888...	[13.31672, 13.247898, 13.045144, 12.724239, 12...	[8.859, 8.63991, 8.336942, 7.95187, 7.500204, ...	[0.103183, 0.21866000000000002, 0.307375, 0.37...	[-5.57998, -5.7986889999999995, -6.033959, -6....	[7.547276, 7.527881, 7.468235, 7.366483, 7.225...	[0.632976, 0.589656, 0.5276609999999999, 0.448...	[-4.3928519999999995, -3.91817, -3.416698, -2....

	age	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
0	19	[-2.768442, -3.496685, -4.069201, -4.462231, -...	[7.935368, 8.292075, 8.685886, 9.081171, 9.438...	[0.6022689999999999, 0.629934, 0.660829, 0.667...	[0.0, -0.002226, -0.00464, -0.0072429999999999...	[0.0, -0.000268, -0.00040699999999999997, -0.0...	[0.0, -0.001321, -0.0026279999999999997, -0.00...	[22.490803, 22.188433, 21.868492, 21.554268, 2...	[-0.010251999999999999, -0.01488, -0.017207, -...	[104.20636, 103.466881, 102.595413, 101.67038,...	[14.214731, 13.85244, 13.169363, 12.26093, 11....	[-3.179904, -3.617692, -4.20662, -4.875635, -5...	[19.032904, 18.2679, 17.341208, 16.317291, 15....	[14.214735, 13.852443, 13.169367, 12.260934, 1...	[-3.179903, -3.61769, -4.206618, -4.875634, -5...	[-19.032904, -18.2679, -17.341208, -16.317291,...	[20.6766, 20.222738, 19.856031, 19.556093, 19....	[-4.393382, -4.227548, -4.138162, -4.11265, -4...	[-0.280891, -1.543496, -2.484963, -3.042148, -...	[2.135388, 1.461672, 1.318257, 1.61889, 2.2538...	[-0.194929, -0.818533, -1.206393, -1.397462, -...	[-14.350356, -12.79824, -11.413175, -10.301519...	[3.991429, 3.891927, 3.795211, 3.698814, 3.599...	[-0.522435, -0.717429, -0.9033939999999999, -1...	[-4.647504, -4.119165, -3.555717, -2.977560999...	[2.708186, 2.7229010000000002, 2.779783, 2.872...	[-8.669715, -8.55989, -8.467485, -8.390489, -8...	[16.503153, 16.36441, 16.326563, 16.373936, 16...	[3.141546, 3.062634, 2.954683, 2.827985, 2.697...	[-1.945191, -1.821488, -1.716818, -1.627697, -...	[-2.919237, -3.102238, -3.3029479999999998, -3...	[7.254066, 7.08643, 6.894266, 6.684382, 6.4677...	[-2.086775, -2.146077, -2.215677, -2.286167, -...	[-7.7877279999999995, -7.421407, -7.040127, -6...	[-5.4825289999999995, -5.833911, -6.063342, -6...	[4.380441, 4.43795, 4.501745, 4.57004, 4.64473...	[2.074525, 3.183789, 4.164791, 4.954755, 5.505...	[0.0, 0.0015400000000000001, 0.003264, 0.00518...	[0.0, 0.000522, 0.001184, 0.001996, 0.002956, ...	[0.0, -0.000998, -0.0020039999999999997, -0.00...	[28.822729, 28.978001, 29.148277, 29.292614, 2...	[0.003681, 0.007298, 0.009037, 0.008681, 0.007...	[97.213318, 96.508972, 95.770767, 95.081993, 9...	[10.846501, 9.729046, 8.539912, 7.347647, 6.20...	[4.045348, 4.578834, 5.088349, 5.536511, 5.904...	[-13.617139, -12.353103, -10.997869, -9.585029...	[10.846505, 9.72905, 8.539916999999999, 7.3476...	[4.045347, 4.578834, 5.088348, 5.53651, 5.9045...	[-13.617139, -12.353103, -10.997869, -9.585029...	[18.893475, 19.141722, 19.27253, 19.289482, 19...	[-2.407445, -2.328364, -2.257316, -2.183507, -...	[-3.71332, -3.729039, -3.720179, -3.685369, -3...	[3.157154, 3.974303, 4.884859, 5.819755, 6.725...	[-0.8432679999999999, -0.736147, -0.5383439999...	[-15.543177, -15.934821, -16.170027, -16.20980...	[0.883767, 1.096725, 1.264113, 1.393558, 1.498...	[-0.365954, -0.43199099999999996, -0.456648999...	[4.044459, 4.620235, 5.210877, 5.8096049999999...	[-5.663974, -6.074879, -6.471653, -6.800329, -...	[-10.194664, -10.122283, -10.048591, -9.980521...	[8.034688, 7.914454, 7.848456, 7.869713, 7.992...	[7.059786, 6.799669, 6.570794, 6.367293, 6.177...	[-0.765127, -0.755408, -0.785045, -0.849924999...	[-4.957033, -5.034188, -5.127676, -5.235561, -...	[8.011764, 7.970358, 7.914809, 7.84646, 7.7663...	[-0.435287, -0.484906, -0.531291, -0.570133, -...	[-0.9707739999999999, -0.470057, 0.02395599999...
1	19	[-7.611665, -8.296784, -8.636337, -8.653679, -...	[6.345243, 6.41904, 6.53749, 6.682159, 6.82977...	[0.015241, 1.130675, 2.046575, 2.656725, 2.912...	[0.016838, 0.015413, 0.013836999999999999, 0.0...	[-0.009348, -0.009233, -0.008978, -0.008558, -...	[0.000483, -0.001058, -0.002572, -0.004029, -0...	[22.925438, 22.503048, 22.122334, 21.826393, 2...	[-0.019576, -0.016118999999999998, -0.01080699...	[102.04879, 101.1464, 100.409393, 99.857368, 9...	[12.341584, 12.335873, 12.083894, 11.615406, 1...	[-5.800112, -5.918781, -6.172589, -6.485641, -...	[26.423983, 25.275766, 23.834427, 22.164652, 2...	[12.341588, 12.335876, 12.083896, 11.615409, 1...	[-5.80011, -5.918779, -6.172588, -6.485639, -6...	[-26.423983, -25.275766, -23.834427, -22.16465...	[20.424934, 19.962803, 19.509947, 19.021643, 1...	[-1.797753, -1.688857, -1.708179, -1.814137999...	[-2.878228, -2.440343, -1.9427249999999998, -1...	[2.762847, 1.953323, 1.724262, 1.937817, 2.447...	[-0.236487, -0.9723259999999999, -1.39173, -1....	[-18.446913, -19.233843, -19.68598, -19.714357...	[1.090251, 0.818932, 0.5202, 0.200094, -0.1280...	[-0.028227, -0.38070299999999996, -0.728997, -...	[-4.88499, -4.3810839999999995, -3.769862, -3....	[3.134768, 3.105432, 3.044675, 2.940096, 2.789...	[-9.577947, -9.449483, -9.323687, -9.206041, -...	[18.54364, 18.746981, 18.860155, 18.887785, 18...	[4.173757, 4.379654, 4.612041, 4.860198, 5.105...	[-1.197732, -0.843793, -0.5067539999999999, -0...	[-2.178959, -2.3056609999999997, -2.478065, -2...	[5.30765, 5.244393, 5.181376, 5.113739, 5.0358...	[-1.020125, -1.011145, -1.010935, -1.014616, -...	[-7.164998, -6.754079, -6.28371, -5.74174, -5....	[-5.628355, -5.81796, -5.811576, -5.627428, -5...	[3.403195, 3.262351, 3.242479, 3.355409, 3.601...	[-3.4450979999999998, -2.903352, -2.27135, -1....	[-0.008744, -0.007463, -0.006006, -0.004360999...	[-0.012428999999999999, -0.01247, -0.012466, -...	[0.002436, 0.001673, 0.000812, -0.000139, -0.0...	[31.828133, 31.580637, 31.387247, 31.242411, 3...	[-0.0011359999999999999, 0.00102, 0.001377, 0....	[90.242859, 90.552948, 91.0634, 91.718605, 92....	[9.194756, 8.696275, 8.075597, 7.379229, 6.644...	[4.146616, 4.320601, 4.622187, 5.041539, 5.558...	[-21.498556, -20.357746, -19.085051, -17.74668...	[9.194759, 8.696277, 8.075601, 7.379233, 6.644...	[4.146615, 4.3206, 4.622186, 5.041538, 5.55823...	[-21.498556, -20.357746, -19.085051, -17.74668...	[18.847454, 18.874477, 18.864149, 18.821054, 1...	[-5.771685, -5.643616, -5.501174, -5.346303, -...	[-7.284528, -7.00043, -6.623891, -6.122418, -5...	[7.355994, 7.557222, 8.092501, 8.881246, 9.836...	[2.684278, 2.574164, 2.540403, 2.580063, 2.684...	[-10.199863, -10.37021, -10.588555, -10.881887...	[-1.597058, -1.574028, -1.5760939999999999, -1...	[-1.239754, -1.412793, -1.553985, -1.665565, -...	[0.427455, 0.937423, 1.446885, 1.9577300000000...	[-3.041614, -3.081234, -3.148071, -3.257658, -...	[-10.598993, -10.551416, -10.489378, -10.40383...	[10.954481, 10.959135, 10.947763, 10.889692, 1...	[8.854305, 8.837968, 8.840726, 8.842192, 8.807...	[-1.246204, -1.063445, -0.88775, -0.716517, -0...	[-5.760704, -5.845308, -5.895671, -5.918782, -...	[7.472655, 7.480461, 7.478623, 7.462115, 7.425...	[-1.736641, -1.716788, -1.6773069999999999, -1...	[-5.447077, -4.997944, -4.516113, -4.006367, -...
2	19	[-4.634666, -5.02648, -5.340784, -5.544887, -5...	[7.019156, 7.207421, 7.360003, 7.428865, 7.382...	[0.8590829999999999, 0.9442879999999999, 1.291...	[0.020581, 0.018594, 0.016465, 0.014183, 0.011...	[-0.041350000000000005, -0.04147000000000001, ...	[-0.002153, -0.003436, -0.004659, -0.005737, -...	[21.892666, 21.667431, 21.510859, 21.407866, 2...	[0.006052, 0.0068579999999999995, 0.0050479999...	[103.10041, 103.414948, 103.769325, 104.036888...	[13.801257, 13.364927, 12.614091, 11.681816, 1...	[-7.515644, -7.981946, -8.420057, -8.740601999...	[19.812559, 19.102261, 18.223965, 17.228157, 1...	[13.801261, 13.36493, 12.614095, 11.681819, 10...	[-7.515642, -7.981944, -8.420057, -8.740601999...	[-19.812559, -19.102261, -18.223965, -17.22815...	[21.490784, 21.206192, 20.810915, 20.239679, 1...	[-1.003782, -1.046244, -1.167168, -1.336805, -...	[1.397087, 2.274668, 2.839696, 3.0362, 2.89127...	[4.38941, 4.258365, 4.485112, 4.878491, 5.2717...	[1.547475, 1.425399, 1.3594089999999999, 1.309...	[-18.630737, -19.466963, -20.113201, -20.44350...	[1.38354, 1.262527, 1.063718, 0.784248, 0.4453...	[0.217076, -0.012333, -0.265026, -0.52868, -0....	[-3.368124, -2.697494, -1.98144, -1.229187, -0...	[1.134091, 0.990254, 0.783542, 0.540679, 0.294...	[-10.493185, -10.305758, -10.112445, -9.931076...	[17.463486, 17.312468, 17.166122, 17.092224, 1...	[3.597836, 3.621159, 3.705733, 3.861809, 4.075...	[-1.154968, -0.9766429999999999, -0.759751, -0...	[-2.844895, -2.9492849999999997, -3.022531, -3...	[5.022056, 4.928493, 4.817266, 4.69589, 4.5721...	[-0.685429, -0.7331249999999999, -0.7694989999...	[-6.30276, -5.719454, -5.056945, -4.33003, -3....	[-6.689217, -7.057434, -7.268858, -7.303356, -...	[4.002138, 4.135075, 4.315419, 4.513762, 4.705...	[-1.061941, -0.874251, -0.6129100000000001, -0...	[-0.010591, -0.008883, -0.006986999999999999, ...	[-0.013071, -0.014, -0.014787, -0.015403, -0.0...	[0.002051, 0.001292, 0.0005059999999999999, -0...	[33.081654, 32.856186, 32.723984, 32.691216, 3...	[0.005253, 0.00417, 0.002565, 0.001298, 0.0009...	[92.021736, 92.243813, 92.391449, 92.442314, 9...	[8.570474, 7.685758, 6.655786, 5.567538, 4.500...	[4.459383, 4.893425, 5.360785, 5.814991, 6.221...	[-14.959339, -13.506869, -12.001929, -10.49032...	[8.570477, 7.685762, 6.65579, 5.56754199999999...	[4.459382, 4.893425, 5.360784, 5.81499, 6.2219...	[-14.959339, -13.506869, -12.001929, -10.49032...	[16.00716, 16.189598, 16.405577, 16.658844, 16...	[-2.608135, -2.575316, -2.595477, -2.666513, -...	[-5.038992, -5.088731, -5.152913, -5.168307, -...	[3.29341, 3.71739, 4.438928, 5.395056, 6.51829...	[0.818074, 0.758255, 0.774924, 0.854435, 0.989...	[-9.693586, -9.014916, -8.393165, -7.926613, -...	[-1.286025, -1.090904, -0.854332, -0.579944999...	[-0.204015, -0.38847899999999996, -0.562819, -...	[1.125047, 1.8016709999999998, 2.525921, 3.273...	[-4.353323, -4.315862, -4.385724, -4.564363, -...	[-10.74014, -10.643498, -10.525556, -10.392888...	[13.31672, 13.247898, 13.045144, 12.724239, 12...	[8.859, 8.63991, 8.336942, 7.95187, 7.500204, ...	[0.103183, 0.21866000000000002, 0.307375, 0.37...	[-5.57998, -5.7986889999999995, -6.033959, -6....	[7.547276, 7.527881, 7.468235, 7.366483, 7.225...	[0.632976, 0.589656, 0.5276609999999999, 0.448...	[-4.3928519999999995, -3.91817, -3.416698, -2....
3	19	[-5.126583, -5.563317, -5.8677600000000005, -6...	[5.791751, 5.315883, 4.801734, 4.333135, 4.000...	[-2.273809, -2.451972, -2.259908, -1.800332, -...	[0.035844999999999995, 0.034800000000000005, 0...	[-0.069149, -0.068253, -0.067149, -0.065811, -...	[-0.002705, -0.003802, -0.00491, -0.005979, -0...	[22.576475, 22.534582, 22.476358, 22.393591, 2...	[-0.0009549999999999999, -0.001529, -0.0022589...	[103.177101, 103.719116, 104.341812, 104.99225...	[14.557133, 13.954084, 13.133732, 12.212157, 1...	[-7.865427, -7.826179, -7.720717, -7.581029, -...	[24.515747, 23.884928, 22.725981, 21.184366, 1...	[14.557137, 13.954086, 13.133736, 12.21216, 11...	[-7.865425, -7.8261769999999995, -7.7207159999...	[-24.515747, -23.884928, -22.725981, -21.18436...	[21.77968, 21.500816, 21.181494, 20.783443, 20...	[0.670709, 0.961919, 1.197635, 1.38975, 1.5488...	[-0.759508, -0.747437, -0.6175849999999999, -0...	[3.097183, 2.726982, 2.64418, 2.753467, 2.9566...	[1.63921, 1.446585, 1.33567, 1.25539, 1.164627...	[-19.076433, -18.702896, -18.339506, -17.93129...	[1.622077, 1.6338110000000001, 1.631074, 1.605...	[1.004183, 0.927782, 0.858348, 0.7985559999999...	[-2.789881, -2.39612, -1.94903, -1.445485, -0....	[-2.787915, -2.747472, -2.713462, -2.687996, -...	[-11.227366, -11.166797, -11.097892, -11.02633...	[21.813576, 21.836899, 21.778131, 21.662382, 2...	[3.321212, 3.2430149999999998, 3.172034, 3.113...	[0.279766, 0.2356, 0.191217, 0.143678, 0.09167...	[-3.42988, -3.314497, -3.187124, -3.06637, -2....	[4.858467, 4.802083, 4.737855, 4.662281, 4.571...	[1.576031, 1.442243, 1.314025, 1.192567, 1.078...	[-6.184168, -5.679617, -5.109772, -4.49029, -3...	[-8.791668, -8.750483, -8.691199, -8.628144, -...	[4.229726, 4.189322, 4.152287, 4.138055, 4.176...	[-0.8796600000000001, -0.09082000000000001, 0....	[-0.0068379999999999995, -0.005461, -0.003916,...	[-0.059211, -0.060008, -0.060662, -0.061166, -...	[0.004118999999999999, 0.0035989999999999998, ...	[35.939461, 35.752632, 35.566681, 35.418114, 3...	[0.002263, 0.002435, 0.0022619999999999997, 0....	[93.980476, 94.029068, 94.107063, 94.192963, 9...	[7.410515, 6.6469059999999995, 5.728951, 4.689...	[5.041845, 5.388712, 5.760208, 6.156972, 6.580...	[-13.847506, -12.840821, -11.775722, -10.68319...	[7.41052, 6.646911, 5.728954, 4.689843, 3.5878...	[5.041844, 5.388711, 5.760208, 6.156972, 6.580...	[-13.847506, -12.840821, -11.775722, -10.68319...	[19.223148, 19.77387, 20.344336, 20.890179, 21...	[-3.799106, -3.6458969999999997, -3.463251, -3...	[-4.025812, -3.748768, -3.553308, -3.454549, -...	[5.193684, 6.295167, 7.574003, 8.958694, 10.36...	[0.979599, 1.074848, 1.164345, 1.227657, 1.248...	[-10.921241, -11.355009, -11.929684, -12.61471...	[-0.868459, -0.653574, -0.407782, -0.149411, 0...	[-0.735085, -0.7949849999999999, -0.8337979999...	[2.640687, 3.018044, 3.416568, 3.837621, 4.279...	[-9.417412, -9.185868, -8.856594, -8.463542, -...	[-11.225997, -11.193144, -11.140943, -11.07178...	[9.740415, 9.96048, 10.225911, 10.527129, 10.8...	[7.465795, 7.240416, 7.00197, 6.767741, 6.5406...	[0.6862579999999999, 0.7500279999999999, 0.801...	[-7.127439, -7.18489, -7.231261, -7.276356, -7...	[6.552842, 6.541142, 6.546217, 6.567391, 6.598...	[0.7637649999999999, 0.772443, 0.790731, 0.815...	[-4.36111, -4.0338, -3.675273, -3.293513, -2.8...

	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
0	[-2.768442, -3.496685, -4.069201, -4.462231, -...	[7.935368, 8.292075, 8.685886, 9.081171, 9.438...	[0.6022689999999999, 0.629934, 0.660829, 0.667...	[0.0, -0.002226, -0.00464, -0.0072429999999999...	[0.0, -0.000268, -0.00040699999999999997, -0.0...	[0.0, -0.001321, -0.0026279999999999997, -0.00...	[22.490803, 22.188433, 21.868492, 21.554268, 2...	[-0.010251999999999999, -0.01488, -0.017207, -...	[104.20636, 103.466881, 102.595413, 101.67038,...	[14.214731, 13.85244, 13.169363, 12.26093, 11....	[-3.179904, -3.617692, -4.20662, -4.875635, -5...	[19.032904, 18.2679, 17.341208, 16.317291, 15....	[14.214735, 13.852443, 13.169367, 12.260934, 1...	[-3.179903, -3.61769, -4.206618, -4.875634, -5...	[-19.032904, -18.2679, -17.341208, -16.317291,...	[20.6766, 20.222738, 19.856031, 19.556093, 19....	[-4.393382, -4.227548, -4.138162, -4.11265, -4...	[-0.280891, -1.543496, -2.484963, -3.042148, -...	[2.135388, 1.461672, 1.318257, 1.61889, 2.2538...	[-0.194929, -0.818533, -1.206393, -1.397462, -...	[-14.350356, -12.79824, -11.413175, -10.301519...	[3.991429, 3.891927, 3.795211, 3.698814, 3.599...	[-0.522435, -0.717429, -0.9033939999999999, -1...	[-4.647504, -4.119165, -3.555717, -2.977560999...	[2.708186, 2.7229010000000002, 2.779783, 2.872...	[-8.669715, -8.55989, -8.467485, -8.390489, -8...	[16.503153, 16.36441, 16.326563, 16.373936, 16...	[3.141546, 3.062634, 2.954683, 2.827985, 2.697...	[-1.945191, -1.821488, -1.716818, -1.627697, -...	[-2.919237, -3.102238, -3.3029479999999998, -3...	[7.254066, 7.08643, 6.894266, 6.684382, 6.4677...	[-2.086775, -2.146077, -2.215677, -2.286167, -...	[-7.7877279999999995, -7.421407, -7.040127, -6...	[-5.4825289999999995, -5.833911, -6.063342, -6...	[4.380441, 4.43795, 4.501745, 4.57004, 4.64473...	[2.074525, 3.183789, 4.164791, 4.954755, 5.505...	[0.0, 0.0015400000000000001, 0.003264, 0.00518...	[0.0, 0.000522, 0.001184, 0.001996, 0.002956, ...	[0.0, -0.000998, -0.0020039999999999997, -0.00...	[28.822729, 28.978001, 29.148277, 29.292614, 2...	[0.003681, 0.007298, 0.009037, 0.008681, 0.007...	[97.213318, 96.508972, 95.770767, 95.081993, 9...	[10.846501, 9.729046, 8.539912, 7.347647, 6.20...	[4.045348, 4.578834, 5.088349, 5.536511, 5.904...	[-13.617139, -12.353103, -10.997869, -9.585029...	[10.846505, 9.72905, 8.539916999999999, 7.3476...	[4.045347, 4.578834, 5.088348, 5.53651, 5.9045...	[-13.617139, -12.353103, -10.997869, -9.585029...	[18.893475, 19.141722, 19.27253, 19.289482, 19...	[-2.407445, -2.328364, -2.257316, -2.183507, -...	[-3.71332, -3.729039, -3.720179, -3.685369, -3...	[3.157154, 3.974303, 4.884859, 5.819755, 6.725...	[-0.8432679999999999, -0.736147, -0.5383439999...	[-15.543177, -15.934821, -16.170027, -16.20980...	[0.883767, 1.096725, 1.264113, 1.393558, 1.498...	[-0.365954, -0.43199099999999996, -0.456648999...	[4.044459, 4.620235, 5.210877, 5.8096049999999...	[-5.663974, -6.074879, -6.471653, -6.800329, -...	[-10.194664, -10.122283, -10.048591, -9.980521...	[8.034688, 7.914454, 7.848456, 7.869713, 7.992...	[7.059786, 6.799669, 6.570794, 6.367293, 6.177...	[-0.765127, -0.755408, -0.785045, -0.849924999...	[-4.957033, -5.034188, -5.127676, -5.235561, -...	[8.011764, 7.970358, 7.914809, 7.84646, 7.7663...	[-0.435287, -0.484906, -0.531291, -0.570133, -...	[-0.9707739999999999, -0.470057, 0.02395599999...
1	[-7.611665, -8.296784, -8.636337, -8.653679, -...	[6.345243, 6.41904, 6.53749, 6.682159, 6.82977...	[0.015241, 1.130675, 2.046575, 2.656725, 2.912...	[0.016838, 0.015413, 0.013836999999999999, 0.0...	[-0.009348, -0.009233, -0.008978, -0.008558, -...	[0.000483, -0.001058, -0.002572, -0.004029, -0...	[22.925438, 22.503048, 22.122334, 21.826393, 2...	[-0.019576, -0.016118999999999998, -0.01080699...	[102.04879, 101.1464, 100.409393, 99.857368, 9...	[12.341584, 12.335873, 12.083894, 11.615406, 1...	[-5.800112, -5.918781, -6.172589, -6.485641, -...	[26.423983, 25.275766, 23.834427, 22.164652, 2...	[12.341588, 12.335876, 12.083896, 11.615409, 1...	[-5.80011, -5.918779, -6.172588, -6.485639, -6...	[-26.423983, -25.275766, -23.834427, -22.16465...	[20.424934, 19.962803, 19.509947, 19.021643, 1...	[-1.797753, -1.688857, -1.708179, -1.814137999...	[-2.878228, -2.440343, -1.9427249999999998, -1...	[2.762847, 1.953323, 1.724262, 1.937817, 2.447...	[-0.236487, -0.9723259999999999, -1.39173, -1....	[-18.446913, -19.233843, -19.68598, -19.714357...	[1.090251, 0.818932, 0.5202, 0.200094, -0.1280...	[-0.028227, -0.38070299999999996, -0.728997, -...	[-4.88499, -4.3810839999999995, -3.769862, -3....	[3.134768, 3.105432, 3.044675, 2.940096, 2.789...	[-9.577947, -9.449483, -9.323687, -9.206041, -...	[18.54364, 18.746981, 18.860155, 18.887785, 18...	[4.173757, 4.379654, 4.612041, 4.860198, 5.105...	[-1.197732, -0.843793, -0.5067539999999999, -0...	[-2.178959, -2.3056609999999997, -2.478065, -2...	[5.30765, 5.244393, 5.181376, 5.113739, 5.0358...	[-1.020125, -1.011145, -1.010935, -1.014616, -...	[-7.164998, -6.754079, -6.28371, -5.74174, -5....	[-5.628355, -5.81796, -5.811576, -5.627428, -5...	[3.403195, 3.262351, 3.242479, 3.355409, 3.601...	[-3.4450979999999998, -2.903352, -2.27135, -1....	[-0.008744, -0.007463, -0.006006, -0.004360999...	[-0.012428999999999999, -0.01247, -0.012466, -...	[0.002436, 0.001673, 0.000812, -0.000139, -0.0...	[31.828133, 31.580637, 31.387247, 31.242411, 3...	[-0.0011359999999999999, 0.00102, 0.001377, 0....	[90.242859, 90.552948, 91.0634, 91.718605, 92....	[9.194756, 8.696275, 8.075597, 7.379229, 6.644...	[4.146616, 4.320601, 4.622187, 5.041539, 5.558...	[-21.498556, -20.357746, -19.085051, -17.74668...	[9.194759, 8.696277, 8.075601, 7.379233, 6.644...	[4.146615, 4.3206, 4.622186, 5.041538, 5.55823...	[-21.498556, -20.357746, -19.085051, -17.74668...	[18.847454, 18.874477, 18.864149, 18.821054, 1...	[-5.771685, -5.643616, -5.501174, -5.346303, -...	[-7.284528, -7.00043, -6.623891, -6.122418, -5...	[7.355994, 7.557222, 8.092501, 8.881246, 9.836...	[2.684278, 2.574164, 2.540403, 2.580063, 2.684...	[-10.199863, -10.37021, -10.588555, -10.881887...	[-1.597058, -1.574028, -1.5760939999999999, -1...	[-1.239754, -1.412793, -1.553985, -1.665565, -...	[0.427455, 0.937423, 1.446885, 1.9577300000000...	[-3.041614, -3.081234, -3.148071, -3.257658, -...	[-10.598993, -10.551416, -10.489378, -10.40383...	[10.954481, 10.959135, 10.947763, 10.889692, 1...	[8.854305, 8.837968, 8.840726, 8.842192, 8.807...	[-1.246204, -1.063445, -0.88775, -0.716517, -0...	[-5.760704, -5.845308, -5.895671, -5.918782, -...	[7.472655, 7.480461, 7.478623, 7.462115, 7.425...	[-1.736641, -1.716788, -1.6773069999999999, -1...	[-5.447077, -4.997944, -4.516113, -4.006367, -...

Portfolio Exam¶

Story¶

Google Colab¶

Privat Computer¶

The Data¶

Initial Data Analysis¶

Preprocessing¶

Exploritory Data Analysis¶

Age Distribution for each Gender¶

Gait Cycles Females and Males¶

Angles in Gait Cylces¶

Correlations¶

Baselines¶

Preprocessing for the Models¶

Evaluation Metric¶

Baseline - Most frequent Class¶

Baseline - SVM¶

Deep Learning Experiments¶

Necessary Steps:¶

Reproducible Experiment¶

Deep Learning Architecture¶

Components of the Architecture¶

Forward Pass:¶

Prediction:¶

Training:¶

Device Management:¶

Initialisation:¶

Data Loader:¶

Training Loop:¶

Validation:¶

Early Stopping¶

Finding Dropout Probabilities¶

Final Model - Tracking with Tensorboard¶

Evaluate the final Model¶

Conclusions and Future Work¶

Conclusion¶

Future Work¶

	subject	sex	created	cycle	Left Ankle Angles_X	Left Ankle Angles_Y	Left Ankle Angles_Z	Left CenterOfMass_corr_X	Left CenterOfMass_corr_Y	Left CenterOfMass_corr_Z	Left Elbow Angles_X	Left Elbow Angles_Y	Left Elbow Angles_Z	Left Foot Pitch Angles_X	Left Foot Pitch Angles_Y	Left Foot Pitch Angles_Z	Left Foot Progression_X	Left Foot Progression_Y	Left Foot Progression_Z	Left Hip Angles_X	Left Hip Angles_Y	Left Hip Angles_Z	Left Knee Angles_X	Left Knee Angles_Y	Left Knee Angles_Z	Left Pelvic Angles_X	Left Pelvic Angles_Y	Left Pelvic Angles_Z	Left Shoulder Angles_X	Left Shoulder Angles_Y	Left Shoulder Angles_Z	Left Thorax Angles_X	Left Thorax Angles_Y	Left Thorax Angles_Z	Left Thorax_Lab Angles_X	Left Thorax_Lab Angles_Y	Left Thorax_Lab Angles_Z	Right Ankle Angles_X	Right Ankle Angles_Y	Right Ankle Angles_Z	Right CenterOfMass_corr_X	Right CenterOfMass_corr_Y	Right CenterOfMass_corr_Z	Right Elbow Angles_X	Right Elbow Angles_Y	Right Elbow Angles_Z	Right Foot Pitch Angles_X	Right Foot Pitch Angles_Y	Right Foot Pitch Angles_Z	Right Foot Progression_X	Right Foot Progression_Y	Right Foot Progression_Z	Right Hip Angles_X	Right Hip Angles_Y	Right Hip Angles_Z	Right Knee Angles_X	Right Knee Angles_Y	Right Knee Angles_Z	Right Pelvic Angles_X	Right Pelvic Angles_Y	Right Pelvic Angles_Z	Right Shoulder Angles_X	Right Shoulder Angles_Y	Right Shoulder Angles_Z	Right Thorax Angles_X	Right Thorax Angles_Y	Right Thorax Angles_Z	Right Thorax_Lab Angles_X	Right Thorax_Lab Angles_Y	Right Thorax_Lab Angles_Z
count	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656	3656
unique	89	2	91	137	2921	2921	2921	2004	2004	2004	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2921	2957	2957	2957	2083	2083	2083	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957	2957
top	sub-069	female	2023-08-08 12:44:55	cycle_1	[6.893189, 6.573399, 6.306869, 6.095495, 5.940...	[2.444143, 2.506672, 2.569635, 2.63392, 2.6998...	[11.521898, 11.749121, 12.024985, 12.331873, 1...	[0.011529, 0.011380999999999999, 0.01116799999...	[-0.010992, -0.011198, -0.011365, -0.011496, -...	[0.006470000000000001, 0.005605, 0.004847, 0.0...	[67.722382, 67.841942, 67.953278, 68.052673, 6...	[0.01161, 0.008588, 0.005424, 0.00264499999999...	[67.336884, 67.013924, 66.85788, 66.890991, 67...	[4.156376, 4.098315, 3.997932, 3.86376, 3.7056...	[-3.6453759999999997, -3.7622679999999997, -3....	[-1.318557, -1.5197289999999999, -1.7227109999...	[4.156377, 4.098315, 3.9979329999999997, 3.863...	[-3.6453759999999997, -3.7622679999999997, -3....	[1.318557, 1.5197289999999999, 1.7227109999999...	[31.564228, 31.398096, 31.248755, 31.126377, 3...	[7.359384, 7.375715, 7.397888, 7.422212, 7.445...	[1.3239239999999999, 1.192659, 1.048491, 0.902...	[22.173082, 21.862728, 21.636713, 21.493454, 2...	[2.108773, 2.00006, 1.9031889999999998, 1.8254...	[-7.5444320000000005, -7.579482, -7.648215, -7...	[12.448437, 12.351758, 12.283578, 12.248773, 1...	[6.319764, 6.260339, 6.21349, 6.176859, 6.1474...	[-6.090747, -5.965488, -5.839082, -5.710993, -...	[18.469509, 18.263018, 18.057535, 17.853489, 1...	[-36.644535, -36.599392, -36.554646, -36.50981...	[-6.003837, -6.064888, -6.121649, -6.179747, -...	[14.478528, 14.447725, 14.380266, 14.271049, 1...	[-1.5006400000000002, -1.540162, -1.590595, -1...	[1.819955, 1.7617509999999998, 1.7148569999999...	[26.989492, 26.855251, 26.713287, 26.563417, 2...	[4.1572379999999995, 4.090972, 4.022262, 3.951...	[-5.144996, -5.084937, -5.018147, -4.943727, -...	[14.523161, 14.508266, 14.497878, 14.492327, 1...	[-11.160184, -11.269531, -11.366331, -11.44471...	[13.599168, 13.521851999999999, 13.468516, 13....	[0.00047000000000000004, 0.0005279999999999999...	[0.001856, 0.0019459999999999998, 0.0019749999...	[0.006651, 0.005778, 0.004824999999999999, 0.0...	[61.774261, 61.173229, 60.599789, 60.090508, 5...	[0.019167999999999998, 0.016928, 0.015477, 0.0...	[97.601425, 97.501183, 97.333618, 97.064056, 9...	[0.220742, 0.201698, 0.188475, 0.180799, 0.178...	[-6.223454, -6.310985, -6.387176, -6.447673, -...	[9.090084, 9.080581, 9.085161, 9.10679, 9.1477...	[0.220744, 0.201699, 0.188476, 0.180801, 0.178...	[-6.223454, -6.310985, -6.387176, -6.447673, -...	[9.090084, 9.080581, 9.085161, 9.10679, 9.1477...	[20.799522, 20.793251, 20.7437, 20.65752, 20.5...	[-3.10834, -3.100393, -3.090199, -3.077057, -3...	[-7.478582, -7.461316, -7.4582429999999995, -7...	[20.235523, 20.212086, 20.171434, 20.115973, 2...	[3.360786, 3.335346, 3.310877, 3.287839, 3.266...	[-9.518766, -9.45668, -9.397385, -9.348419, -9...	[12.843493, 12.866432, 12.861176, 12.832086, 1...	[-5.967627, -5.997739, -6.024588, -6.047569, -...	[13.495292, 13.477127, 13.465643, 13.461199, 1...	[21.69734, 22.23702, 22.759691, 23.227921, 23....	[-31.540339, -31.487875, -31.409733, -31.32809...	[-19.184639, -19.156521, -19.19298, -19.269421...	[12.455172, 12.51285, 12.602796, 12.717604, 12...	[-0.8873709999999999, -0.8704419999999999, -0....	[-7.802131, -7.800966, -7.7954740000000005, -7...	[25.967037, 26.047792, 26.133192, 26.220331, 2...	[-3.387635, -3.392554, -3.40273, -3.418771, -3...	[6.05367, 6.047386, 6.048463, 6.056428, 6.0703...
freq	134	1930	134	112	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3

	age
count	3656.000000
mean	54.342177
std	24.618695
min	0.000000
25%	30.000000
50%	62.000000
75%	74.000000
max	93.000000

	age
count	91.000000
mean	52.450549
std	23.708069
min	0.000000
25%	29.000000
50%	58.000000
75%	72.500000
max	93.000000

	algorithm	dataset	Accuracy	AUC
0	Baseline	train	51.729	50.0
1	Baseline	val	51.729	50.0
2	Baseline	test	51.799	50.0